ICBRA 2019 CONFERENCE ABSTRACT
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CONFERENCE ABSTRACT
2019 6th International Conference on Bioinformatics
Research and Applications (ICBRA 2019)
December 19-21, 2019
Seoul National University, Seoul, South Korea
Organized by
Supported by
Published and Indexed by
http://www.icbra.org/
ICBRA 2019 CONFERENCE ABSTRACT
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Conference Venue
Building 25-1, College of Natural Sciences, Seoul National University
Addr.: Building. 25-1 College of Natural Science, Seoul National University 1 Kwanak-ro
Kwanak-gu Seoul, 08826 South Korea
How to Get Here?
Way #1 From the Airport
(1). Incheon International Airport
• Take the ―#6003 Airport limousine bus‖ at Incheon International Airport
Get off the limousine at the main gate of Seoul National University
• Take the ―#6017 Airport limousine bus‖ at Incheon International Airport
Get off the limousine at Hoam Faculty House.
(2). Gimpo International Airport
• Take the ―#6003 Airport limousine bus‖ or ―#651 blue bus‖at Gimpo International Airport
Get off the bus at the main gate of Seoul National University
• Take the subway from Gimpo International Airport. on the No.5 line
Transfer to the No.2 line at Yeongdeungpo-gu Office Station.
Get off at either Seoul National University Station or Nakseongdae Station
Way #2 From Seoul or Yeongdeungpo Station
(1). Seoul Station
• (Take the Bus) Take the ―#501, #750A, or #750B blue bus‖ at Seoul Station
Get off at the main gate of Seoul National University
• Take the No.4 line towards Sadang
Transfer to the No.2 line at Sadang Station.
Get off at the main gate of Seoul National University Station.
(2). Yeongdeungpo Station
• Get on subway Line No.1
Transfer to the No.2 line at Sindorim Station
Get off at the Seoul National University Station.
Way #3 From Express Bus Terminal
(1). Seoul Express Bus Terminal or Cental City Terminal
• Get on ―#8541 green bus or #643 blue bus‖
ICBRA 2019 CONFERENCE ABSTRACT
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Transfer to the ―#5528 green bus‖ at Sadang 1-dong Gwanak-market Station
Get off at the main gate of Seoul National University
• Get on the No.3 subway line at Express Bus Terminal Station
Transfer to the No.2 line at Seoul National University of Education Station.
Exit 3 at Seoul National University Station
Use shuttle bus, city bus, or taxi.
(2). Dong-Seoul Bus Terminal
• Use the No.2 subway line at Gangbyeon Station
Get off at Seoul National University Station
Use shuttle bus, city bus, or taxi.
ICBRA 2019 CONFERENCE ABSTRACT
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Map
ICBRA 2019 CONFERENCE ABSTRACT
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Table of Contents Introduction 10
Conference Committee 11
Program at-a-Glance 13
Presentation Instruction 17
Keynote Speaker Introduction 18
Invited Speaker Introduction 25
Oral Session on December 19, 2019
Session 1: Medical Informatics
K0019: Massive Metagenomic Data Analysis using Microbiota and Machine
Learning
Tae-Hyuk Ahn
34
K0023: Evaluating Model-free Directional Dependency Methods on Single-cell
RNA Sequencing Data with Severe Dropout
Eliška Dvorakova, Sajal Kumar, Jiri Klema, Filip Železny, Karel Drbal and
Mingzhou Song
34
K0029: Study of Characterization of Promiscuous Binding Sites in Protein-small
Molecule Complexes
Yoichi Murakami
35
K5003: Protein Tertiary Structure Modeling Driven by Deep Learning and Contact
Distance Prediction in CASP13
Jianlin Cheng
35
K0005: Identifying the Best Metrics to Find the Best Quality Clusters of Genes from
Gene Expression Data
Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam, Tanzima
Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad and Md Abdul Mottalib
36
Oral Session on December 20, 2019
Session 2: Computational Engineering and Biochemistry
K2009: Ammount and Differentiation of Cihateup Ducks Leukocytes That Fed
Supplemented with Mangosteen Peel Extract Microcapsules
Andri Kusmayadi
37
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K2008: Eco-physiological and Cytological Responses in Medicinal Species
Onopordum Alexandrinum and Alhagi Graecorum after Seed Exposure to Static
Magnetic Field
Migahid M M, El-Bakatoshi R F, Megahed S M, Amin A W and El-Sadek L M
37
K2016: Biochemical and Microbial Change in Food Fermentation ‗Ubi Karet Busuk‘
Sumba, East Nusa Tenggara, Indonesia
Periskila Dina Kali Kulla and Endah Retnaningrum
38
K0013: Computer Administered Banana Flour Processing System
Gamaliel Eve R. Minggong, Arjay D. Pabalinas, Hadassah Alysson F. Tesoro,
Randy E. Angelia and Hanna Leah P. Angelia
38
K0017: Metastatic State of Colorectal Cancer can be Accurately Predicted with
Methylome
Somayah Albaradei, Maha Thafar, Christophe Van Neste, Magbubah Essac and,
Vladimir B. Bajic
39
K4012: QCKer: An x86-AVX/AVX2 Implementation of Q-gram Counting Filter for
DNA Sequence Alignment
Joven L. Pernez Jr., Roger Luis Uy, Kaizen Vinz A. Borja and Jan Carlo G.
Maghirang
39
Session 3: Statistical Genetics
K1020: Molecular Classification of Transcriptome Expression in Serous Ovarian
Cancer using Unsupervised Clustering
Jisun Lim and Taesung Park
41
K1021: Hierarchical Component Models of Pathway Analysis for RNA Sequencing
Data
Lydia Mok, Sungyoung Lee and Taesung Park
41
K1022: Hierarchical Structural Component Model with 3-layers for
SNP-gene-pathway Analysis
Nan Jiang, Sungyoung Lee, Heungsun Hwang and Taesung Park
42
K1023: Predicting Individual Risk of Malignancy in the Patients with Intraductal
Papillary Mucinous Neoplasms of the Pancreas using Automated Machine Learning
Chanhee Lee, Hae Seung Kang, Jin-Young Jang and Taesung Park
43
K1024: The Predictive Model using Extracellular Vesicles (EVs) Microbiome
Successfully Predict Matched Pancreatic Ductal Adenocarcinoma (PDAC) and
Non-cancerous Sample
Kyulhee Han, Nayeon Kang, Jae Ri Kim, Jin-Young Jang and Taesung Park
43
K2015: A Visible Neural Network to Guide Precision Medicine
Kuenzi BM, Park J, Fong S, Ma J, Kreisberg JF and Ideker T 44
Session 4: Biomedical Engineering
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K4013: An EEG-based Depression Detection Method using Machine Learning
Model
Ran Bai, Yu Guo, Xianwu Tan, Lei Feng and Haiyong Xie
45
K4020: Identification of Raw EEG Signal for Prosthetic Hand Application
Azizi Miskon, Ayu Kusuma Sari Djonhari, Satria Mohd Haziq Azhar, Suresh A/L
Thanakodi and Siti Nooraya Mohd Tawil
45
K4024: Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial
Visual Presentation Task
Bowen Li, Zhiwen Liu, Xiaorong Gao and Yanfei Lin
46
K0011: Development of Arduino Microcontroller-based Safety Monitoring Prototype
in the Hard Hat
Robert D. Arcayena Jr, Alessis D. Ballarta, Kendall N.Claros and Rodrigo S.
Pangantihon Jr.
46
K4009: Improvement of the BT-Heartomotive Device for Avert Car Accident using
MYBradyTachyHeart Mobile Application
Mohd Azrul Hisham Mohd Adib, Muhammad Irfan Abdul Jalal and Nur Hazreen
Mohd Hasni
47
K1004: Contributions of Novel Nanomaterials to Pharmaceutical Analysis
Yixin Zhang 47
Session 5: Bioinformatics
K4014: Automated SNOMED CT Mapping of Clinical Discharge Summary Data for
Cardiology Queries in Clinical Facilities
Abdul Aziz Latip, Ma. Stella Tabora Domingo, 'Ismat Mohd Sulaiman and
Tengku Nurulhuda Tengku Abd Rahim
49
K4008: Acceptability of Virtual Reality among Older People: Ordinal Logistic
Regression Study from Taiwan
Diana Barsasella, Shankari Priya Chakkaravarthi, Hee-Jung Chung, Mina Hur,
Shabbir Syed Abdul, Shwetambara Malwade, Chia-Chi Chang, Megan F. Liu and
Yu-Chuan Li
49
K1010: Identification of Key Genes Associated with Kidney Cancer Through
Pan-cancer Bioinformatics Analysis
Nur Ain Rodzi and Suresh Kumar
50
K0008: Visualization of Differential Arm-specific miRNA Expression with TCGA
Dataset
Chao-Yu Pan and Wen-Chang Lin
50
K0030: The Method of Organizing a Service-oriented User Interface for Multi-agent
Information and Control Systems
Iakov S. Korovin, Donat Ya. Ivanov and Sergei A. Semenistyi
51
K0031: Implementation of Fingerprint Recognition using Convolutional Neural
Network and RFID Authentication Protocol on Attendance Machine
Maredi Aritonang, Irwan Doni Hutahaean, Hasudungan Sipayung and Indra
51
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Hartarto Tambunan
Session 6: Image Analysis
K0003: Identification and Classification of Export Quality Carabao Mangoes
Johannie Ave P. Ardepolla, Mike Jhon Reymar Cortez, Abigail L. Escorpion,
Jetron J. Adtoo and Kimberly M. Nepa
53
K0009: A Supervised Learning Approach on Rice Variety Classification using
Convolutional Neural Networks
Louie John L. Castillo, Juvy Amor M. Galindo and Jamie Eduardo C. Rosal
53
K0010: De-husked Coconut Quality Evaluation using Image Processing and
Machine Learning Techniques
Tito C. Lim Jr., Jaedy O. Torregosa, Aubrey Rose A. Pescadero and Rodrigo S.
Pangantihon Jr.
54
K0018: Data Mining of Daily Pig Behaviors using Wireless IC Tag based
Monitoring System in Pig Farms
Geunho Lee, Atsushi Ishimoto, Shinsuke H. Sakamoto and Seiji Ieiri
54
K0002: Supervised Machine Learning Approach for Pork Meat Freshness
Identification
Christell Faith D. Lumogdang, Christell Faith D. Lumogdang, Stephone Jone S.
Loyola, Randy E. Angelia and Hanna Leah P. Angelia
55
K0004: Automated Vermiculture Monitoring and Compost Segregating System using
Microcontrollers
Menkent S. Barcelon, Alvin A. Orilla, Jessabelle A. Mahilum and Jetron J.
Adtoon
55
Poster Session on December 20, 2019
K4010: Discrimination Colonies of Staphylococcus Aureus and Salmonella Enterica
by using Machine Learning
Manao Bunkum and Sarinporn Visitsattapongse
57
K5002: The Noninvasive Blood Glucose Monitoring by Means of Near Infrared
Sensors
Jindapa Nampeng, Yanisa Samona, Chuchart Pintavirooj, Baorong Ni and
Sarinporn Visitsattapongse
57
K4018: In Vivo Performance and Biocompatibility of an Intelligent Artificial Anal
Sphincter System
Ding Han, Guo-Zheng Yan and Kai Zhao
58
K4022: Optimization of the Treatment of Chronic Eczema in the Elderly
Zhumash Nurmukhambetov, Torgyn Ibrayeva, Alibek Nurmukhambetov and
Yerlan Bazarbekov
58
K4025: The Efficacy and Safety of Long-term Aspirin Use for Cancer Primary
Prevention: An Updated Systematic Review and Subgroup Meta-analysis of 59
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Randomized Controlled Trials
Qibiao Wu, Xiaojun Yao, Hongwei Chen and Elaine Lai-Han Leung
K1005: Rational Design of NOT-gate in Tri-node Enzyme Regulatory Networks
Xiao Wang and Xudong Lv 60
K2007: Genetic Mutations Associated with Diffuse Large B-cell Lymphoma
Jinghan Qiu 60
K4006: Comparison of Two Different Kernel Functions of Support Vector
Regression for Tracking Tumor Motion: Radial Basis Function and Linear Function
Jie Zhang, Xue Bai and Guoping Shan
60
K4007: The Accuracy Heart Dosimetric Study of Left-breast Cancer Radio-therapy
using Deformable Image Registration
Xue Bai, Shengye Wang, Binbing Wang and Jie Zhang
61
K4016: Druggability of Intrinsically Disordered Proteins and Their Virtual Screening
Strategy
Yutong Wan
62
K4019: Multiple Absorption Spectra Modeling Method for Improving Model
Stability in Spectral Analysis
Yongshun Luo, Gang Li and Ling Lin
62
Note 63
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Introduction
Welcome to 2019 6th International Conference on Bioinformatics Research and Applications (ICBRA 2019) which is organized by Biology and Bioinformatics Society (BBS) under Hong Kong Chemical, Biological & Environmental Engineering Society (CBEES), supported by Enterprise promoting world leading major departments and Information (ISSN: 2078-2489). The objective of ICBRA 2019 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Bioinformatics Research and Applications.
Papers will be published in the following conference proceedings:
ACM Conference Proceedings (ISBN: 978-1-4503-7218-3): archived in ACM Digital
Library, indexed by EI Compendex and SCOPUS, and submitted to be reviewed by Thomson
Reuters Conference Proceedings Citation Index (ISI Web of Science).
Some excellent papers will be recommended for reviewing of publication in one of following
journals:
Information (ISSN: 2078-2489) as a special issue, which can be indexed by Scopus
(Elsevier), EI Compendex, Emerging Sources Citation Index (ESCI-Web of Science), etc.
Genomics and Informatics (GNI, eISSN: 2234-0742) as a special issue, which can be
indexed by PubMed, PubMed Central, Scopus, Google Scholar, etc.
Conference website and email: http://www.icbra.org/; [email protected]
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Conference Committee
Conference Chairs Prof. Weizhong Li, Sun Yat-sen University, China
Prof. Taesung Park, Seoul National University, South Korea
Prof. Seungyoon Nam, Gachon University, South Korea
Assoc. Prof. Sungho Won, Seoul National University, South Korea
Program Chairs Prof. Qiang Fang, Shantou University, China
Prof. Taejin Ahn, Handong Global University, South Korea
Dr. Zhou Jianhong, Xihua University, China
Technical Committee Prof. Max Garzon, University of Memphis, USA
Prof. Kui Zhang, Michigan Technological University, USA
Prof. Yusen Zhang, Shandong University, China
Prof. Shihua Zhang, Chinese Academy of Sciences, China
Assoc. Prof. Adam G. Polak, Wrocław University of Science and Technology, Poland
Assoc. Prof. Hongyan Xu, Augusta University, USA
Assoc. Prof. Haijun Gong, Saint Louis University, USA
Assoc. Prof. Zhifu Sun, Mayo Clinic (Rochester, MN), USA
Assoc. Prof. Wen-Chang Lin, Yang-Ming University, Taiwan
Assist. Prof. Giuditta Franco, Verona University, Italy
Assist. Prof. Wooyoung Kim, University of Washington, USA
Assist. Prof. Xuan Guo, University of North Texas, USA
Dr. Asmita Sautreau, University of Portsmouth, UK
Dr. Joel P. Arrais, University of Coimbra, Portugal
Dr. Tony Smith, University of Waikato, New Zealand
Dr. Marissa Gray, Stevens Institute of Technology, USA
Dr. Wen Zhang, Icahn School of Medicine at Mount Sinai, USA
Dr. Yangyang Hao, Veracyte Inc., USA
Prof. Juan M Corchado, University of Salamanca, Spain
Dr. Jianghan Qu, Veracyte Inc., USA
Assist. Prof. Monwadee Wonglapsuwan, Prince of Songkla University, Thailand
Assoc. Prof. Yi Guo, Fudan University, China
Assoc. Prof. Yunping Zheng, South China University Of Technology, China
Dr. Balamurugan Shanmugam, Head-Reseach and Development, QUANTS - IS & CS, India
Dr. Phan Duy Hung, FPT University, Vietnam
Assoc. Prof. Jiang Gui, Dartmouth College, USA
Dr. Le Nguyen Quoc Khanh, Nanyang Technological University, Singapore
Joe Song, New Mexico State University, USA
Assist. Prof. Lijun Cheng, Ohio State University, USA
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Assoc. Prof. Dukka KC, North Carolina A&T State University, USA
Dr. Richard Edwards, University of New South Wales, Australia
Assoc. Prof. Ng To Yee Vincent, Hong Kong Polytechnic University, Hong Kong
Prof. Dongbo Bu, Chinese Academy of Sciences, China
Dr. Ka-Chun Wong, City University of Hong Kong, Hong Kong
Prof. M. Sohel Rahman, Bangladesh University of Engineering & Technology (BUET),
Bangladesh
Assoc. Prof. Gan G Redhi, Durban University of Technology, South Africa
Assoc. Prof. Shuai Cheng Li, City University of Hong Kong, Hong Kong
Dr. Chen Li, Monash University, Australia
Dr. Tae-Hyuk Ahn, Saint Louis University, USA
Assist. Prof. Mutwil Marek, Nanyang Technological University, Singapore
Prof. Bechan Sharma, University of Allahabad, India
Dr. Yasser EL-Manzalawy, Penn State University, USA
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Program at-a-Glance
December 18,
2019
(Wednesday)
Time Arrival Registration & Practice Room
(Building 26, Room102)
09:00-17:00
Short Course
Prof. Michael Greenacre, Universitat Pompeu Fabra,
Spain
Topic: ―Compositional Data Analysis in Practice I‖
December 19,
2019
(Thursday)
09:00-12:00
Short Course
Prof. Michael Greenacre, Universitat Pompeu Fabra,
Spain
Topic: ―Compositional Data Analysis in Practice II‖
December
19, 2019
(Thursday)
10:00-17:00 Arrival Registration (Lobby of Building 25-1, 1F)
Afternoon Conference (Building 25-1, International Meeting Room)
13:30-13:40
Opening Remarks
Prof. Taesung Park, Seoul National University,
South Korea
13:40-14:20
Keynote Speech I
Prof. Sun Kim, Seoul National University, South Korea
Topic: ―Measuring Intra-Tumor Heterogeneity from Bulk
Cell Sequencing‖
14:20-15:00
Keynote Speech II
Prof. Hans-Uwe Dahms
Kaohsiung Medical University, Taiwan
Topic: ―Evaluation of In silico Toxicity Predictions‖
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December
19, 2019
(Thursday)
15:00-15:30 Coffee Break & Group Photo
Time
International
Meeting Room
(Building 25-1)
Room 105
(Building 25)
15:30-15:55
Invited Speech I
Prof. Chuhsing Kate
Hsiao, National Taiwan
University, Taiwan
Topic: ―Network Analysis
for Prioritizing Regulation
Association of Hub Gene
Nodes‖
Invited Speech V
Dr. Seungyoon Nam,
Gachon University,
South Korea
Topic: ―Systems Biology in
Early Drug Discovery‖
15:55-16:20
Invited Speech II
Prof. Tzu-Pin Lu, National
Taiwan University, Taiwan
Topic: ―A Novel
Algorithm to Identify
Regulating ceRNAs using
the Integration of miRNA
and Gene Expression
Profiles‖
Session 1
Topic: ―Medical
Informatics‖
5 presentations
16:20-16:45
Invited Speech III
Dr. Minsun Song,
Sookmyung Women's
University, South Korea
Topic: ―Goodness of Fit
Test at Extreme of Disease
Risk Distribution‖
16:45-17:10
Invited Speech IV
Dr. Wonil Chung, Soongsil
University, South Korea
Topic: ―Efficient Penalized
Regression Approaches
Improve Polygenic
Prediction in Biobank Data
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December
20, 2019
(Friday)
Arrival Registration (Lobby of Building 25-1, 1F)
Morning Conference (Building 25-1, International Meeting Room)
09:30-09:40
Opening Remarks
Prof. Taesung Park, Seoul National University,
South Korea
09:40-10:20
Keynote Speech III
Prof. Chanchal K. Mitra, University of Hyderabad, India
Topic: ―Kinetic Modeling of Sodium Glucose
Co-transport‖
10:20-10:50 Coffee Break & Group Photo
10:50-11:30
Keynote Speech IV
Prof. Michael Greenacre, Universitat Pompeu Fabra,
Spain
Topic: ―The Analysis of High-Dimensional Microbiome
Data: It's A Question of Coherence!‖
11:30-12:10
Keynote Speech V
Prof. Taesung Park, Seoul National University,
South Korea
Topic: ―Hierarchical Component Analysis for
Microbiome Data Using Taxonomy Information‖
12:10-13:30 Lunch (Restaurant)
Afternoon Conference
Time
International
Meeting Room
(Building 25-1)
Room 105
(Building 25)
13:30-14:00
Invited Speech VI
Dr. Sungho Won,
Seoul National University,
South Korea
Topic: ―Phylogenetic
Tree-based Microbiome
Association Test‖
Session 2
Topic: ―Computational
Engineering and
Biochemistry‖
6 presentations
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December
20, 2019
(Friday)
14:00-14:30
Invited Speech VII
Prof. Yujin Chung,
Kyonggi University,
South Korea
Topic: ―Inference of
Isolation-with-migration
Models from Genomic
Data
Session 2
Topic: ―Computational
Engineering and
Biochemistry‖
6 presentations
(-continued)
14:30-15:00
Invited Speech VIII
Dr. Iksoo Huh, College of
Nursing and Research
Institute of Nursing
Science, Seoul National
University, South Korea
Topic: ―Enhanced
permutation approach via
pruning‖
15:00-15:15 Coffee Break and Poster Session
Time
International
Meeting Room
(Building 25-1)
Room 105
(Building 25)
15:15-16:45
Session 3
Topic: ―Statistical
Genetics‖
6 presentations
Session 4
Topic: ―Biomedical
Engineering‖
6 presentations
16:45-17:00 Coffee Break and Poster Session
17:00-18:30
Session 5
Topic: ―Bioinformatics‖
6 presentations
Session 6
Topic: ―Image Analysis‖
6 presentations
18:30-20:00 Dinner (Restaurant)
December
21, 2019
(Saturday)
10:00-11:30
Academic Visit
Graduate School of Public Health,
Medical science & Bioinformatics Lab.
Tips: Please arrive at the Conference Room 10 minutes before the session begins to upload PPT into
the laptop; submit the poster to the staff when signing in.
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Presentation Instruction
Instruction for Oral Presentation
Devices Provided by the Conference Organizer:
Laptop Computer (MS Windows Operating System with MS PowerPoint and Adobe Acrobat
Reader); Digital Projectors and Screen; Laser Stick
Materials Provided by the Presenters:
PowerPoint or PDF Files (Files should be copied to the Conference laptop at the beginning of
each Session.)
Duration of each Presentation (Tentatively):
Keynote Speech: about 35 Minutes of Presentation and 5 Minutes of Question and Answer
Invited Speech: about 20/25 Minutes of Presentation and 5 Minutes of Question and Answer
Oral Presentation: about 12 Minutes of Presentation and 3 Minutes of Question and Answer
Instruction for Poster Presentation
Materials Provided by the Conference Organizer:
The place to put poster
Materials Provided by the Presenters:
Home-Made Posters: Submit the poster to the staff when signing in; Poster Size: A1
(841*594mm); Load Capacity: Holds up to 0.5 kg
Best Presentation Award One Best Oral or Poster Presentation will be selected from each session, and the Certificate
for Best Presentation will be awarded at the end of the session on Dec. 19 and Dec. 20, 2019.
Dress Code Please wear formal clothes or national representative of clothing.
Disclaimer Along with your registration, you will receive your name badge, which must be worn when
attending all conference sessions and activities. Participants without a badge will not be
allowed to enter the conference venue. Please do not lend your name badge to the persons
who are not involved in the conference and do not bring the irrelevant persons into the
conference venue.
The conference organizers cannot accept liability for personal injuries, or for loss or damage
of property belonging to conference participants, either during, or as a result of the conference.
Please check the validity of your own insurance.
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Keynote Speaker Introduction
Keynote Speaker I
Prof. Sun Kim
Seoul National University, South Korea
Sun Kim is Professor in the School of Computer Science and Engineering, Director of
Bioinformatics Institute, and an affiliated faculty for the Interdisciplinary Program in
Bioinformatics at Seoul National University. Before joining SNU, he was Chair of Faculty
Division C; Director of Center for Bioinformatics Research, an Associate Professor in School
of Informatics and Computing; and an Adjunct Associate Professor of Cellular and Integrative
Physiology, Medical Sciences Program at Indiana University (IU) Bloomington. Prior to
joining IU in 2001, he worked at DuPont Central Research from 1998 to 2001, and at the
University of Illinois at Urbana-Champaign from 1997 to 1998. Sun Kim received B.S and
M.S and Ph.D in Computer Science from Seoul National University, KAIST and the
University of Iowa, respectively.
Topic: “Measuring Intra-Tumor Heterogeneity from Bulk Cell Sequencing”
Abstract—Intratumor heterogeneity (ITH) represents various phenotypic diversity among
subclones that constitute a cancer tissue. ITH is now considered as an important clinical factor
related to the aggressiveness, drug resistance, recurrence, and metastasis of cancer. Since
cancer is a disease of the genome, the ITH level and cancer subclonal structure are inferred
based on the genomic profile (e.g. somatic mutations and copy number variations). However,
recent studies have suggested that the ITH can be identified at multi-omics level. Recently,
our group developed ITH inference methods for methylome, transcriptome, and spliceome
bulk-tumor sequencing data. The first method (Scientific Report 2016) that we developed was
a transcriptomic ITH (tITH) model that measured entropy of biological network states. We
developed another information theoretic method for measuring spliceomic ITH (sITH) in
cancer cells, SpliceHetero (PLoS ONE 2019). Splicing patterns in cancer are very
complicated, including wide spread retention of intron sequences in transcripts. The last one,
PRISM (ISMB/Bioinformatics 2019), is a tool for inferring the composition of epigenetically
distinct subclones of a tumor solely from methylation patterns obtained by reduced
representation bisulfite sequencing.
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Keynote Speaker II
Prof. Hans-Uwe Dahms
Kaohsiung Medical University, Taiwan
Dr. Hans-Uwe Dahms is a professor at Kaohsiung Medical University. He is interested in
stress responses in general and within aquatic systems in particular. He, his colleagues and
students integratively study pollution and the toxicology of stressors from physical, chemical,
and biological sources. He is equally interested in climate change, the spread of diseases,
antibiotic-resistance, food and drink safety from water sources, and integra-tive approaches in
environmental and public health monitoring, risk assessment and management. He advised
more than 25 Ph.D. students in their research and published more than 275 papers in scientific
journals. He served as a reviewer for more than 70 SCI journals, as editorial board member of
12 reputed scientific journals, academic editor of PLosONE, and as editor in chief of
FRONTIERS in Marine Pollution.
Topic: “Evaluation of in silico Toxicity Predictions”
Abstract—Chemoinformatics represents a search for chemical information resources where
data are typically transformed into information and this into technologies that allow to make
decisions better and faster. Such in silico approaches refer to computer applications or
computer simulations. In silico approaches in pollution studies can best be understood as
chemoinformatics using informational techniques applied to a range of problems in the field
of chemistry related to toxicology and the effects of pollutants. To provide an example for the
evaluation of in silico approaches, we will introduce to food safety issues related to food
preservatives, plasticizers, and artificial sweeteners. For such assessments SMILES of the
above food additives will be taken from the PubChem database. By using MarvinSketch all
chemicals presented here are based on structural data retrieved from PubChem. In silico
predictive models generally provide fast and economic screening tools for compound
properties. They allow a high throughput and a constant optimization. They are less expensive,
less time consuming, have a high reproducibility, and reduce experimental efforts.
Computational approaches can also prioritize chemicals for their toxicological evaluation in
order to reduce the amount of costly in vitroand ethically problematic in vivo toxicological
screenings, and provide early alerts for newly developed substances. Limitations include that
ADME aspects (absorption, distribution, metabolism, and excretion – which are basic
pharmacokinetic descriptors) are not taken into account. There can be a lack of quality and
ICBRA 2019 CONFERENCE ABSTRACT
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transparency of the training set of experimental data. The programs, descriptors, and
applicabilities are sometimes not clear. In addition are carcinogenicity predictions not possible
based on non-genotoxic compounds.
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Keynote Speaker III
Prof. Chanchal K. Mitra
University of Hyderabad, India
Chanchal Kumar Mitra (born November 02, 1950; W Bengal, India) is currently
(2015-2017) an UGC (University Grants Commission, Government of India) Emeritus
Professor at the Department of Biochemistry, University of Hyderabad. He obtained his B.Sc.
(Bachelor of Science) degree from the Presidency College (University of Calcutta) with
Chemistry as the main subject (1969) and his M.Sc. (Master of Science) degree from the same
university in 1971 in Pure Chemistry. He did his doctoral work at the Tata Institute of
Fundamental Research, Bombay (now Mumbai) on computational studies on the
conformations of several aza-nucleosides and received his Ph.D. degree from the University
of Bombay in 1977. He did post doctoral work at the University at Albany (New York, USA)
and University of Lund (Sweden). He joined the University of Hyderabad in 1985 and retired
in 2015. His current research interests are (I) biosensors and (II) modeling of metabolic
pathways. He has a number of publications in the relevant areas which can be found from
https://www.researchgate.net/profile/Chanchal_Mitra/contributions.
Topic: “Kinetic Modeling of Sodium Glucose Co-transport”
Abstract— A simulation of the kinetics of the sodium-glucose transporters has been reported
using a model widely used in literature. However, the various kinetic constants of the
transporter have been replaced by 1 (as they are not available in the literature). We have also
studied the effect of the membrane potential on glucose transport. The used model is leaky,
i.e., sodium transport can take place independently of glucose transport. Although the results
can be considered only semi-quantitative, we find that glucose transport is rather
energy-intensive, because around 15 sodium ions needed to be transported for each glucose
molecule carried inside. However, the process is powerful, in the sense that the final glucose
concentration outside can fall almost to zero
ICBRA 2019 CONFERENCE ABSTRACT
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Keynote Speaker IV
Prof. Michael Greenacre
Universitat Pompeu Fabra, Spain
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra in Barcelona.
His whole career has been devoted to research in multivariate analysis and he has written six
books on correspondence analysis and data visualization and co-edited four more with Prof.
Jörg Blasius (Bonn University). He has over 100 published articles in peer-reviewed journals
and books, and has given short courses in 15 countries to statisticians, biologists and social
scientists, in Europe, North and South America, Africa and Australia. For more than 30 years
he has been working in projects related to Arctic ecology, based in north Norway. And for
almost 20 years he has become interested in compositional data analysis, collaborating with
biochemists, geochemists and recently with researchers in the analysis of microbiome data.
Topic: “The Analysis of High-Dimensional Microbiome Data: It's A Question of
Coherence!”
Abstract—The standard structure of a microbiome data set is: (1) high-dimensional (hundreds
or thousands of variables, in the form of operational taxonomics units, or OTUs); (2)
relatively small sample (tens or hundreds); (3) basic data are counts of OTUs in each
sampling unit; (4) many zeros (50-90% of the data set are zeros); and (5) the totals in each
sampling unit are irrelevant, it is the relative counts that are important. To try to understand
these data and extract some meaning from them, the problem might either be (a) to identify
the OTUs that are driving the overall structure, which means equivalently removing those
OTUs that can be considered random and uninteresting; (b) when there is some specific
objective such as to explain a response variable or distinguish between pre-defined groups, to
identify the OTUs that are relevant to this objective. In either case we have the challenge of
variable selection. In this talk I will describe the approach to such data known as
compositional data analysis. The basic principle of this approach is that the analytical
procedure be subcompositionally coherent, which dictates that ratios of OTUs be used rather
than the OTUs themselves. The problem with this approach is that zero values are not
permitted, so there are various strategies to cope with this situation. One way is to replace the
zeros by some small positive values, while a pragmatic solution is to use an alternative
approach for which zeros need no replacement, while deviating as little as possible from the
ideal requirement of subcompositional coherence.
ICBRA 2019 CONFERENCE ABSTRACT
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Keynote Speaker V
Prof. Taesung Park
Seoul National University, South Korea
Prof. Taesung Park received his B.S. and M.S. degrees in Statistics from Seoul National
University (SNU), Korea in 1984 and 1986, respectively and received his Ph.D. degree in
Biostatistics from the University of Michigan in 1990. From Aug. 1991 to Aug. 1992, he
worked as a visiting scientist at the NIH, USA. From Sep. 2002 to Aug. 2003, he was a
visiting professor at the University of Pittsburgh. From Sep. 2009 to Aug. 2010, he was a
visiting professor in Department of Biostatistics at the University of Washington. From Sep.
1999 to Sep. 2001, he worked as an associate professor in Department of Statistics at SNU.
Since Oct. 2001 he worked as a professor and currently the Director of the Bioinformatics and
Biostatistics Lab. at SNU. He served as the chair of the bioinformatics Program from Apr.
2005 to Mar. 2008, and the chair of Department of Statistics of SNU from Sep. 2007 and Aug.
2009. He has served editorial board members and associate editors for the international
journals including Genetic Epidemiology, Computational Statistics and Data Analysis,
Biometrical Journal, and International journal of Data Mining and Bioinformatics. His
research areas include microarray data analysis, GWAS, gene-gene interaction analysis, and
statistical genetics.
Topic: “Hierarchical Component Analysis for Microbiome Data Using Taxonomy
Information”
Abstract—The recent advent of high-throughput sequencing technology has enabled us to
study the associations between human microbiome and diseases. The DNA sequences of
microbiome samples are clustered as operational taxonomic units (OTUs) according to their
similarity. The OTU table containing counts of OTUs present in each sample is used to
measure correlations between OTUs and disease status and find key microbes for prediction
of the disease status. Various statistical methods have been proposed for such microbiome
data analysis. However, none of these methods have used hierarchical structure of taxonomy
information that is biologically meaningful. In this paper, we propose a hierarchical structural
component model for microbiome data (HisCoM-microb) using taxonomy information as
well as OTU table data. The proposed HisCoM-microb consists of two layers: one for OTUs
grouped at the lowest taxonomy level and the other for OTUs grouped at the higher taxonomy
level. Then we calculate simultaneously coefficient estimates of OTUs of all layers inserted in
the hierarchical model. Through this analysis, we can infer the association between OTUs and
ICBRA 2019 CONFERENCE ABSTRACT
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disease status, considering the impact of taxonomic structure on disease status. Both
simulation study and real microbiome data analysis show that our method provides a new
testing approach for microbiome data which clearly reveal the relations between each taxon
and disease status at the same time as finding the key microbiota of the disease.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker Introduction
Invited Speaker I
Prof. Chuhsing Kate Hsiao
National Taiwan University, Taiwan
Chuhsing Kate Hsiao, professor at National Taiwan University, received her PhD in
Statistics at Carnegie Mellon University and then joined the Division of Biostatistics in
Institute of Epidemiology and Preventive Medicine, College of Public Health in 1994. She
was the Associate Dean of the college and Department Head of Public Health in 2011-2013.
She served in the ICSA Board of Directors from 2014 to 2016, and was awarded Distinguish
Professor in 2015. Her research interests focus on the development of methodology for
genetic association studies, including Bayesian mixture models for GWAS, regularized
support vector regression for gene selection, Hamming distance-based clustering algorithm,
integrative analysis of multi-omics genomic variants and network/pathway analysis for
multiple genetic markers. She also enjoys inter-disciplinary collaborations such as the
national myopic survey, risk evaluation of air pollutant and temperature on coronary heart
diseases and probabilistic ensemble forecast of typhoon precipitation.
Topic: “Network Analysis for Prioritizing Regulation Association of Hub Gene Nodes”
Abstract—To identify and prioritize the influential hub genes in a gene-set or biological
pathway, most analyses rely on calculation of marginal effects or tests of statistical
significance. Such procedures may be inappropriate if dependence between gene nodes exists,
and if the hub nodes require more attention than others. The highly connected hub genes may
play a more important role for the whole network to function properly. To prioritize the hub
gene nodes, here we develop a pathway activity score incorporating the local effect of gene
nodes as well as intra-network affinity measures. This score summarizes the expression levels
in a gene-set/pathway for each sample, with weights on local and network information,
respectively. The score is next used to examine the impact of each node through a
leave-one-out evaluation. Two cancer studies, one involving RNA-Seq from breast cancer
patients with high-grade ductal carcinoma in situ and one microarray expression data from
ovarian cancer patients, and simulation analysis are used to assess the performance of the
ICBRA 2019 CONFERENCE ABSTRACT
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procedure, and to compare with existing methods with/without consideration of correlation
and network information.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker II
Prof. Tzu-Pin Lu
National Taiwan University, Taiwan
Prof. Tzu-Pin Lu got his Ph.D. degree in the institute of Biomedical Engineering and
Bioinformatics, National Taiwan University. He served as a postdoc fellow for the YongLin
Biomedical Engineering Center, National Taiwan University and the Division of
Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug
Administration, USA. After that, he joined the institute of Epidemiology and Preventive
Medicine, National Taiwan University and currently he is an associate professor. His major
research interests include utilizing high-through genomics and genetics data to study different
diseases, such as breast cancer, lung cancer and cardiovascular diseases. In addition, he
developed several online databases, analytical systems and R packages to facilitate analyzing
genetics data.
Topic: “A Novel Algorithm to Identify Regulating ceRNAs using the Integration of miRNA
and Gene Expression Profiles”
Abstract—In recent years, researchers can examine multiple omics data in the same individual.
Among different molecules, microRNA (miRNA) is the most studied non-coding RNA.
Several studies report the regulatory effect of one miRNA to its target genes depends on its
own miRNA expression level, which is named as a competing endogeneous RNA (ceRNA)
and miRNA pair. Currently, most algorithms need to define different groups based on the
expression level of the miRNA. However, challenge arises; the expression level of a miRNA
is actually a continuous variable instead of a discrete variable. To address this issue, we
developed a novel algorithm to identify ceRNA-miRNA pairs. First, a random walk method
was used to exclude miRNA-gene pairs without any correlation. Subsequently, a circular
binary segmentation algorithm was applied to obtain the peaks of the miRNA expression
levels across different samples. A simulation study with different scenarios demonstrated that
our algorithm is efficient and accurate to identify true ceRNA-miRNA pairs. Lastly, two real
cancer datasets from The Cancer Genome Atlas (TCGA) were analyzed by our algorithm. The
results suggest that our approach not only is able to validate previous findings from other
studies but also can reveal several new ceRNA-miRNA candidates.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker III
Dr. Minsun Song
Sookmyung Women's University, South Korea
Dr. Song received a B.S. and M.S. in statistics from Seoul National University and a Ph.D. in
statistics from the University of Chicago. After that, she worked as a postdoctoral research
associate at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. Dr.
Song joined the Biostatistics Branch, Division of Cancer Epidemiology and Genetics,
National Cancer Institute, National Institutes of Health as a postdoctoral fellow and later
joined Department of Mathematics and Statistics at University of Nevada Reno as an assistant
professor. Dr. Song now serves an assistant professor at Department of Statistics at
Sookmyung Women's University. Her current research is focused on development of
statistical methodologies with genetic or genomic data. Especially, her statistical research
interests include analysis of high-dimensional data in the presence of latent variables,
high-dimensional large-scale modeling, and testing for gene-gene and gene-environment
interaction.
Topic: “Goodness of Fit Test at Extreme of Disease Risk Distribution”
Abstract—Risk-prediction models need careful calibration to ensure they produce unbiased
estimates of risk for subjects in the underlying population given their risk-factor profiles. As
subjects with extreme high or low risk may be the most affected by knowledge of their risk
estimates, checking the adequacy of risk models at the extremes of risk is very important for
clinical applications. We propose a new approach to test model calibration targeted toward
extremes of disease risk distribution where standard goodness-of-fit tests may lack power due
to sparseness of data. We construct a test statistic based on model residuals summed over only
those individuals who pass high and/or low risk thresholds and then maximize the test statistic
over different risk thresholds. We derive an asymptotic distribution for the max-test statistic
based on analytic derivation of the variance-covariance function of the underlying Gaussian
process. The method is applied to a large case-control study of breast cancer to examine joint
effects of common single nucleotide polymorphisms (SNPs) discovered through recent
genome-wide association studies. The analysis clearly indicates a non-additive effect of the
SNPs on the scale of absolute risk, but an excellent fit for the linear-logistic model even at the
extremes of risks.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker IV
Dr. Wonil Chung
Soongsil University, South Korea
Wonil Chung is an assistant professor in Statistics at Soongsil University. I was a research
associate at Harvard T.H. Chan School of Public Health and received Ph.D. in Biostatistics
from the University of North Carolina at Chapel Hill and MS, BS in Statistics from Seoul
National University. As my early career, I worked for an IT company as a computer
programmer and thus have programming skill in C/C++, Java, Python, R as well as parallel
computing. During my doctoral and postdoctoral years, I have developed novel statistical
methodologies for genome-wide association studies (GWAS), quantitative trait loci (QTL),
expression QTL (eQTL) mapping and genomic risk prediction. Also, I have participated in a
variety of large-scale omics projects such as identification of shared genetic architecture and
analyses of methylation and metabolomics data.
Topic: “Efficient Penalized Regression Approaches Improve Polygenic Prediction in
Biobank Data”
Abstract—We introduce CTPR (Cross-Trait Penalized Regression), a powerful and practical
approach for multi-trait polygenic risk prediction in Biobank-scale cohorts. Specifically, we
propose a novel cross-trait penalty function with Lasso and MCP to incorporate the shared
genetic effects across multiple traits for large-sample GWAS data. Our approach extracts
information from the secondary traits that is beneficial for predicting the primary trait based
on individual-level genotypes and/or summary statistics. Our novel implementation of a
parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS
data. Next, we extend our CTPR method to multi-ethnic GWAS data by modelling
population-specific LD. The predictive performance of CTPR (Cross-eThnic Penalized
Regression) will be compared with the existing multi-ethnic prediction methods.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker V
Assist. Prof. Seungyoon Nam
Gachon University, South Korea
Seungyoon Nam received the PhD degree in bioinformatics from Seoul National University,
in 2008. From 2008 to 2010, he was a postdoctoral fellow with the Indiana University School
of Medicine, Indiana, performing computational cancer epigenomics studies. From 2010 to
2015, he worked as a senior researcher in a field of cancer bioinformatics at the Korea
Institute of Information and Science Technology, and the National Cancer Center of Korea.
Since 2015, he has been an assistant professor in the College of Medicine, Gachon University,
Korea. His research interests include systems biology, miRNA biology, Next-Generation
Sequencing (NGS) clinical tests, and druggable genome in various diseases. He has served as
a member of the program committee at the IEEE International Conference on Bioinformatics
& Biomedicine, Workshop on Data Mining from Genomic Rare Variants and Its Application
to Genome-Wide Analysis since 2014
Topic: “Systems Biology in Early Drug Discovery”
Abstract—Biologists have studied individual biological entities. It partly resulted from no
available public datatasets regarding their entities in interests. But, the situation has been
changed drastically in cancer. High-throughput sequencing datasets (i.e., ―big data‖) have
been poured in public data repositories, and the datasets are now available freely.
Accumulation of these datasets have now included experimental measurements (e.g., mRNA
levels, mutations) for exhaustively diverse biological entities (so called ―Omics‖) including
biologists‘ own entities in interests. From these high-throughput datasets, a global
understanding (equivalently, system-level understanding or systems biology) of molecular
mechanisms is now allowed in phenotype changes in interests. In this talk, systems biology
will be introduced in the field of medicine. Finally, association of systems biology and
druggable target discovery will be discussed shortly.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker VI
Sungho Won
Seoul National University, South Korea
Topic: “Phylogenetic Tree-based Microbiome Association Test”
Abstract—Motivation: Ecological patterns of the human microbiota exhibit high inter-subject
variation, with few operational taxonomic units (OTUs) shared across individuals. To
overcome these issues, non-parametric approaches, such as the Mann–Whitney U-test and
Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases.
However, these approaches only use the ranks of observed relative abundances, leading to
information loss, and are associated with high false-negative rates. In this study, we propose a
phylogenetic tree-based microbiome association test (TMAT) to analyze the associations
between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate
patterns of similarity among different OTUs, and TMAT provides an efficient method for
utilizing such information for association analyses. The proposed TMAT provides test
statistics for each node, which are combined to identify mutations associated with host
diseases.
Results: Power estimates of TMAT were compared with existing methods using extensive
simulations based on real absolute abundances. Simulation studies showed that TMAT
preserves the nominal type-1 error rate, and estimates of its statistical power generally
outperformed existing methods in the considered scenarios. Furthermore, TMAT can be used
to detect phylogenetic mutations associated with host diseases, providing more in-depth
insight into bacterial pathology.
Availability: The 16S rRNA amplicon sequencing metagenomics datasets for colorectal
carcinoma and myalgic encephalomyelitis/chronic fatigue syndrome are available from the
European Nucleotide Archive (ENA) database under project accession number PRJEB6070
and PRJEB13092, respectively. TMAT was implemented in the R package. Detailed
information is available at http://healthstat.snu.ac.kr/software/tmat.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker VII
Dr. Yujin Chung
Kyonggi University, South Korea
Dr. Yujin Chung is a tenure-track assistant professor in the Department of Applied Statistics
at Kyonggi University, South Korea. Her research areas include statistical phylogenetics,
biostatistics, bioinformatics, and Bayesian analysis. Dr. Chung received a Ph. D. in Statistics
from the University of Wisconsin - Madison, USA. She was a postdoctoral fellow and a
research assistant professor at the center for computational genetics and genomics (CCGG) at
Temple University, USA. Before moving back to South Korea, she was a tenure-track
assistant professor in the Department of Statistics at Auburn University, USA.
Topic: “Inference of Isolation-with-migration Models from Genomic Data”
Abstract—Isolation-with-migration (IM) models explain the divergence of populations by the
processes of genetic drift and migrations. Due to recent sequencing and computing advances,
statistical inference has played an important role in the study of evolutionary history from
genomic data. However, typical analyses are either limited to a small amount of data or fail to
estimate complex and diverse evolutionary models. In this talk, I will present a new Bayesian
method for estimating IM models including population sizes, splitting time of two populations,
and migration rates. The new method resolves statistical limitations and overcomes major
roadblocks to analyze genome-scale data. Using importance sampling and a Markov chain
representation of genealogy, the new method scales to genomic data without mixing difficulty
in a Markov chain Monte Carlo simulation. I will demonstrate the new method with simulated
data and real DNA sequences.
ICBRA 2019 CONFERENCE ABSTRACT
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Invited Speaker VIII
Dr. Iksoo Huh
Seoul National University, South Korea
Iksoo Huh received his Ph.D. degree in Statistics from Seoul National University, South
Korea, in 2015, and he was a postdoctoral fellow at the School of Biological Sciences,
Georgia Institute of Technology, USA.
Topic: “Enhanced permutation approach via pruning”
Abstract— Big multi-omics data for bioinformatics area consists of huge numbers of features,
but relatively small number of samples. In addition, the features from multi-omics data have
their own specific characteristics depending on whether they are from genomics, proteomics,
metabolomics, and so forth. Due to these various characteristics, standard statistical analysis
approaches based on parametric assumptions may sometimes fail to provide exact asymptotic
results. In order to resolve the issue, permutation test can be a way to exact analysis of
multi-omics data, because it is distribution-free and flexible to use. In permutation tests,
p-values are evaluated by estimating location of test statistic in an empirical null distribution
which is generated by random shuffling. However, the permutation approach can be infeasible
when number of features becomes larger, because more stringent control of the type I error for
multiple hypothesis testing is needed, and consequently much larger number of permutation is
required to reach the significant level. To address the problem, we propose a well-organized
strategy for enhanced permutation tests via multiple pruning (ENPP). ENPP prunes the
features in every permutation round if they are determined to be non-significant. In other
words, if a feature has more times that statistics from permuted data sets exceed the original
statistics than a certain number of pre-determined cutoff, it will be determined to be
non-significant, and ENPP removes the feature and iterates the process without the feature in
the next permutation round. Our simulation study showed that the ENPP method could
remove about 50% features at the first permutation round, and in the 100th permutation round,
98% of the features were removed and only 7.4% of computation time was required when
compared to original unpruned permutation approach. In addition, we applied this approach to
a real data set (Korea Association REsource: KARE) which has 327,872 SNPs to find
association with a non-normal distributed phenotype (fasting plasma glucose, FPG),
interpreted the results, and discussed feasibility and advantage of the approach.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 1
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 19, 2019 (Thursday)
Time: 15:55-17:10
Venue: Room 105
Topic: “Medical Informatics”
Session Chair: Prof. Hans-Uwe Dahms
K0019
Session 1
Presentation 1
(15:55-16:10)
Massive Metagenomic Data Analysis using Microbiota and Machine
Learning
Tae-Hyuk Ahn
Saint Louis University, USA
Abstract—Metagenomics is the application of modern genomic techniques
to investigate the members of a microbial community directly in their
natural environments and is widely used in many studies to survey the
communities of microbial organisms that live in diverse ecosystems. In
order to understand the metagenomic profile of one of the densest
interaction spaces for millions of people, the MetaSUB International
Consortium has collected and sequenced metagenomes from subways of
different cities across the world. To distinguish the metagenomic profiling
among different cities and also predict unknown samples precisely based on
the profiling, two different approaches are proposed using machine learning
techniques; one is a read-based taxonomy profiling of each sample and
prediction method, and the other is a reduced representation assembly-based
method. Among various machine learning techniques tested, the random
forest technique showed promising results as a suitable classifier for both
approaches with 98% research topics. We also developed a versatile R
package to analyze massive and diverse microbiome profiles of
metagenomics samples quickly and accurately using machine learning.
Based on the interactive web-supporting library, R-Shiny, the proposed
software provides user-friendly functions and options for data
preprocessing, model development, model validation, and independent
sample prediction.
K0023
Session 1
Presentation 2
Evaluating Model-free Directional Dependency Methods on Single-cell
RNA Sequencing Data with Severe Dropout
Eliška Dvoˇrakova, Sajal Kumar, Jiˇri Klema, Filip Železny, Karel Drbal
and Mingzhou Song
New Mexico State University, USA
ICBRA 2019 CONFERENCE ABSTRACT
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(16:10-16:25)
Abstract—As severe dropout in single-cell RNA sequencing (scRNAseq)
degrades data quality, current methods for network inference face increased
uncertainty from such data. To examine how dropout influences directional
dependency inference from scRNA-seq data, we thus studied four methods
based on discrete data that are model-free without parametric model
assumptions. They include two established methods: conditional entropy
and Kruskal-Wallis test, and two recent methods: causal inference by
stochastic complexity and function index. We also included three
non-directional methods for a contrast. On simulated data, function index
performed most favorably at varying dropout rates, sample sizes, and
discrete levels. On an scRNA-seq dataset from developing mouse cerebella,
function index and Kruskal-Wallis test performed favorably over other
methods in detecting expression of developmental genes as a function of
time. Overall among the four methods, function index is most resistant to
dropout for both directional and dependency inference. The next best
choice, Kruskal-Wallis test, carries a directional bias towards a uniformly
distributed variable. We conclude that a method robust to marginal
distributions with a sufficiently large sample size can reap benefits of
single-cell over bulk RNA sequencing in understanding molecular
mechanisms at the cellular resolution.
K0029
Session 1
Presentation 3
(16:25-16:40)
Study of Characterization of Promiscuous Binding Sites in Protein-small
Molecule Complexes
Yoichi Murakami
Tokyo University of Information Sciences, Japan
Abstract—An exhaustive comparison of different proteins has provided new
insights into the characteristics of many proteins, leading to understanding
their molecular and biological functions. Although many research works
have so far characterized binding sites (BS) in proteins, only a few
research-works about promiscuous BS which can accommodate different
ligands or compounds have been presented and the knowledge is still
limited. Thus, in this study, the promiscuous BS in protein-small molecule
complexes from the Protein Data Bank (PDB) were exhaustively compared
with the non-promiscuous BS to reveal physicochemical and structural
properties of their BS. As a result, aliphatic, aromatic, and sulfur-containing
amino acids (AA) were more likely to appear in promiscuous BS, indicating
that they tend to be more hydrophobic than non-promiscuous BS.
Furthermore, the number of AA and the accessible surface area of
promiscuous BS tended to be larger than those of non-promiscuous BS. In
addition, the significant difference of α-helix between promiscuous BS and
non-promiscuous BS was observed.
K5003
Session 1
Protein Tertiary Structure Modeling Driven by Deep Learning and Contact
Distance Prediction in CASP13
Jianlin Cheng
University of Missouri, USA
ICBRA 2019 CONFERENCE ABSTRACT
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Presentation 4
(16:40-16:55)
Abstract—Predicting residue‐residue distance relationships (e.g., contacts)
has become the key direction to advance protein structure prediction since
2014 CASP11 experiment, while deep learning has revolutionized the
technology for contact and distance distribution prediction since its debut in
2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced
our MULTICOM protein structure prediction system with three major
components: contact distance prediction based on deep convolutional neural
networks, distance‐driven template‐free (ab initio) modeling, and protein
model ranking empowered by deep learning and contact prediction. Our
experiment demonstrates that contact distance prediction and deep learning
methods are the key reasons that MULTICOM was ranked 3rd out of all 98
predictors in both template‐free and template‐based structure modeling in
CASP13. The success of MULTICOM system clearly shows that protein
contact distance prediction and model selection driven by deep learning holds
the key of solving protein structure prediction problem. However, there are
still challenges in accurately predicting protein contact distance when there
are few homologous sequences, folding proteins from noisy contact
distances, and ranking models of hard targets.
K0005
Session 1
Presentation 5
(16:55-17:10)
Identifying the Best Metrics to Find the Best Quality Clusters of Genes from
Gene Expression Data
Raihanoor Reza Rayon, Joydhriti Choudhury, Md. Tawhidul Islam,
Tanzima Rahman Roshni, Faisal Bin Ashraf, Rasif Ajwad and Md Abdul
Mottalib
Brac University, Bangladesh
Abstract—With the recent advancement of computing technique and data
availability in the field of computational biology, it has been a great
opportunity for the scientists to find the evolutionary relation among the
living beings in terms of their genotypic and phenotypic attributes.
Microarray, one of the efficient ways to store the expression level of genes
in the living being, can be used to create groups from a set of genes based on
their phenotypic information. This information plays an important role in
pathway analysis, disease prediction, target identification in drug design and
many other important functionalities and applications in biology. However,
it has become a great challenge over time to select a particular distance
metric to calculate the similarity between the genes. In this work, we have
studied 16 possible combinations of metrics to find the groups of similar
genes in terms of their expression level by building their phylogenetic
relation and keeping the most related genes together. Moreover, we have
validated our findings by evaluating the output of the same trials on
different data sets. We have found that, for grouping the similar genes
together by building a Phylogenetic Tree, Maximum Distance Metric and
Average Linkage tends to give the best quality.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 2
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 20, 2019 (Friday)
Time: 13:30-15:00
Venue: Room 105
Topic: “Computational Engineering and Biochemistry”
Session Chair: Prof. Chanchal K. Mitra
K2009
Session 2
Presentation 1
(13:30-13:45)
Ammount and Differentiation of Cihateup Ducks Leukocytes That Fed
Supplemented with Mangosteen Peel Extract Microcapsules
Andri Kusmayadi
Universitas Padjadjaran, Indonesia
Abstract—This study aimed to examine the effects of the level of
mangosteen peel extract microcapsules (MPEM) on the amount and
differentiation of Cihateup duck leukocytes. The treatments were tested
consisting of 0% MPEM (T1), 0.5% MPEM (T2), 1.0% MPEM (T3), 1.5%
MPEM (T4), 2.0% MPEM (T5), 2.5% MPEM (T6) and 50 ppm bacitracin
as positive control (T7). The research data were tested using ANOVA
method and continued by Duncan test if there was a significant difference.
In the leukocytes amount test, the MPEM treatment did not have a
significant effect (P>0.05). Meanwhile, MPEM treatment had a significant
effect (P<0.05) on all leukocytes differentiation parameters (heterophils,
eosinophils, basophils, lymphocytes, and monocytes). This proved that the
MPEM treatment had the ability to improve the leukocytes differentiation of
Cihateup ducks. In this study, the level of 2.0% MPEM had better results
than the other treatments.
K2008
Session 2
Presentation 2
(13:45-14:00)
Eco-physiological and Cytological Responses in Medicinal Species
Onopordum Alexandrinum and Alhagi Graecorum after Seed Exposure to
Static Magnetic Field
Migahid M M, El-Bakatoshi R F, Megahed S M, Amin A W and El-Sadek L
M
Alexandria University, Egypt
Abstract—Two medicinal plants Onopordum alexandrinum Boiss. (Family
Asteraceae) and Alhagi graecorum Medic. (Family Fabaceae) were selected
to determine the effect of different intensities and exposure times of static
magnetic field on eco-physiological and cytological levels. The results
indicated that shoot and root lengths of O. alexandrinum decreased
ICBRA 2019 CONFERENCE ABSTRACT
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significantly in contrast to A. graecorum, where the length increased
gradually with magnetic field compared to control. A significant reduction
of chlorophyll a was recorded in A. graecorum in response to treatments;
however significant variation in chlorophyll content of O. alexandrinum was
recorded. The total phenolic content of O. alexandrinum decreased
significantly compared to control; while in the case of A. graecorum high
significant accumulation was recorded. The total flavenoid content in the
two species exhibited a significant reduction due to changing exposure time
of magnetic field. Significant increases in the mitotic index of both species
root meristems were recorded under seed treatments. The highest intensity
induced significant increases in chromosomal aberrations of O.
alexandrinum but different intensity showed highly significant increase in A.
graecorum. Remarkable trends were recorded toward higher tolerance in A.
graecorum compared to O. alexandrinum under magnetic effect. This opens
an unusual perspective on plant adaptation that should be tested in other
species.
K2016
Session 2
Presentation 3
(14:00-14:15)
Biochemical and Microbial Change in Food Fermentation ‗Ubi Karet
Busuk‘ Sumba, East Nusa Tenggara, Indonesia
Periskila Dina Kali Kulla and Endah Retnaningrum
Universitas Gadjah Mada, Indonesia
Abstract—Ubi karet busuk is a traditional food fermentation product from
Sumba, East Nusa Tenggara, Indonesia which uses cassava as a substrate.
This substrate was fermented spontaneously by indigenous microorganisms
(bacteria, yeast, fungi). During the fermentation, these microorganisms
released enzymes such as amylase, protease, lipase, and hydrolyzed
polysaccharides, proteins, lipids into digestible products with a pleasant,
attractive taste and texture for human consumption. The biochemical aspects
were investigated during the fermentation, including reducing sugar, protein,
lactic acid, and pH values. The results of observations show that the bacteria
that are primarily involved in the fermentation process are lactic acid
bacteria. 15 isolates were identified phenotypically as lactic acid bacteria.
The results showed that reducing sugar levels increased from 0.09 to 0.60
mg/mL, protein levels increased from 0.08 to 0.27 mg/mL, lactic acid
increased from 1.08 % to 11.88 %, pH decreased from 6.9 to 3.8.
K0013
Session 2
Presentation 4
(14:15-14:30)
Computer Administered Banana Flour Processing System
Gamaliel Eve R. Minggong, Arjay D. Pabalinas, Hadassah Alysson F. Tesoro,
Randy E. Angelia and Hanna Leah P. Angelia
University of Mindanao, Philippines
Abstract—Class C Cavendish Bananas or Musa cavendischii though are
mostly deemed worthless, can be processed into food-grade banana flour.
This can be done through dehydration and pulverization. Some institutions
have been doing this but lacks the efficiency of automated technology for
they have been making use of manual dehydration processes like sun drying
ICBRA 2019 CONFERENCE ABSTRACT
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& household oven drying, resulting into massive longevity of banana flour
processing. The developed system is an automated integration of
microprocessor into mechanical machinery, processing bananas into banana
flour with an interactive user interface—efficiently speeding up the usually
manual process with a single click. Reducing the dehydrating time
exponentially from 24-48 hours to 2 & a half hours at 90° C and the milling
time was timed at 3 minutes at the most for 1 kg of peeled input, showing
the efficiency of the processing system through the statistical analysis of the
data gathered from multiple testing. Therefore, the system is capable of
providing the promised improvement in the efficiency of banana flour
production.
K0017
Session 2
Presentation 5
(14:30-14:45)
Metastatic State of Colorectal Cancer can be Accurately Predicted with
Methylome
Somayah Albaradei, Maha Thafar, Christophe Van Neste, Magbubah
Essack and Vladimir B. Bajic
King Abdullah University of Science and Technology, Saudi Arabia
Abstract—Colorectal cancer (CRC) appears to be the third most common
cancer as well as the fourth most common cause of cancer deaths in the
world. Its most lethal states are when it becomes metastatic. It is of interest
to find tests that can quickly and accurately determine if the patient has
already developed metastasis. Changes in methylation profiles have been
found to be characteristic of cancers at different stages and can therefore be
used to develop diagnostic panels. We developed a deep learning (DL)
model (Deep2Met) using methylation profiles of patients with CRC to
predict if the cancer is in its metastatic state. Results suggest that our
method achieves an AUPR and an average F-score of 96.99% and 94.71%,
respectively, making Deep2Met potentially useful for diagnostic purposes.
The DL model Deep2Met we developed, shows promise in the diagnosis of
CRC based on methylation profiles of individual patients.
K4012
Session 2
Presentation 6
(14:45-15:00)
QCKer: An x86-AVX/AVX2 Implementation of Q-gram Counting Filter for
DNA Sequence Alignment
Joven L. Pernez Jr., Roger Luis Uy, Kaizen Vinz A. Borja and Jan Carlo G.
Maghirang
De La Salle University, Philippines
Abstract—The paper presents the implementation of the q-gram counting
filter using x86-AVX/AVX2 SIMD instructions. There are three novel
findings during the course of the research work. First, to eliminate
inconsistency between the theoretical and experimental result, synthetic
reads are generated using DNA character ―T‖ only since generated synthetic
reads create a random condition in which the number of seed instances is
variable, and thus cannot be predicted. Second, the presence and absence
of various SIMD parameters namely, prefetch, multithreading and AVX
instruction sets are introduced to determine the speed factor. Result shows
ICBRA 2019 CONFERENCE ABSTRACT
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that there is a 2% speedup with the presence of prefetching, a 2.7% speedup
with the presence of AVX instruction sets, a 100.41% speedup with the
presence of multithreading, and a 112.25% speedup if all parameters are
used. This shows that multithreading has the biggest effect among the said
parameters. Third, the x86-AVX is compared with Razers3, an existing read
mapper using q-gram counting filter. In terms of filter only, the x86-AVX
is 12x faster than the Razers3 for small seed size of 4. Though, Razers3
outperforms the x86-AVX implementation for longer seed (i.e., seed size of
12). This is attributed to Razers3 being optimized for q-gram of 12 or
higher. From these findings, it is recommended that using real datasets is
preferred over synthetic datasets. Also, implementation using
multithreading approach is recommended. Though future work can be
done to compare multithread with FPGA implementation.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 3
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 20, 2019 (Friday)
Time: 15:15-16:45
Venue: International Meeting Room
Topic: “Statistical Genetics”
Session Chair: To be added
K1020
Session 3
Presentation 1
(15:15-15:30)
Molecular Classification of Transcriptome Expression in Serous Ovarian
Cancer using Unsupervised Clustering
Jisun Lim, Taesung Park
The Research Institute of Basic Sciences, Seoul National University, Seoul,
Korea.
Department of Statistics, Seoul National University, Seoul, Korea.
Abstract—Differentially expressed mRNAs have been found to be
associated with in the development and progression of cancer. In order to
improve chemotherapeutic treatment in serous ovarian cancer, it is needed to
identify suitable biomarkers and potential drug targets. Transcriptome
expression data obtained by RNA sequencing are analyzed. We use an
unsupervised clustering technique, such as nonnegative matrix factorization
(NMF) to identify subtypes of ovarian cancer. We demonstrate results of
NMF and discovered patterns of gene expression in hidden biological
mechanism. We further identify potential mRNA biomarkers for predicting
survival.
K1021
Session 3
Presentation 2
(15:30-15:45)
Hierarchical Component Models of Pathway Analysis for RNA Sequencing
Data
Lydia Mok, Sungyoung Lee, Taesung Park
Interdisciplinary Program in Bioinformatics, Seoul National University,
Seoul 08826, South Korea.
Center for Precision Medicine, Seoul National University Hospital, 101
Daehak-ro Jongno-gu, Seoul, South Korea
Department of Statistics, Seoul National University, Seoul 08826, South
Korea.
Abstract—In the recent years, technical improvements and decreasing costs
of next - generation sequencing technology made RNA sequencing
(RNA-seq) an alternative to the microarrays. Many number of methods and
ICBRA 2019 CONFERENCE ABSTRACT
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software were proposed for identification of differentially expressed genes
(DEGs). However, analyzing high-throughput gene expression data at the
pathway level can be effective. Identifying active pathways that differ
between two conditions can have more explanatory power than a simple list
of DEGs. Several analyses for identifying cancer-associated pathways based
on gene expression data are mostly based on single pathway analyses, and
thus do not consider correlations between pathways. In this study, we
propose a hierarchical structural component model of pathway analysis for
RNA sequencing data for binary phenotype which accounts for the
hierarchical structure of genes and pathways in single model considering the
correlations among pathways simultaneously. The main goal of this study
was finding significant pathways that are relevant to the diagnosis of cancer.
In application to a real biological data analysis, we demonstrated that our
method could successfully identify pathways associated with diagnosis of
cancer.
K1022
Session 3
Presentation 3
(15:45-16:00)
Hierarchical Structural Component Model with 3-layers for
SNP-gene-pathway Analysis
Nan Jiang, Sungyoung Lee, Heungsun Hwang, Taesung Park
Interdisciplinary Program in Bioinformatics, Seoul National University,
Seoul 08826, Korea
Center for Precision Medicine, Seoul National University Hospital, 101
Daehak-ro Jongno-gu, Seoul, South Korea
Department of Psychology, McGill University, 2001 Avenue McGill
College, Montreal, Quebec H3A 1G1, Canada
Department of Statistics, Seoul National University, Seoul 08826, Korea
Abstract—For genome-wide association study (GWAS), gene-based and
pathway-based analyses of common variants have been widely used to
enhance interpretation of the phenotype-related genetic variants. However,
most of these methods often neglect the SNP-gene-pathway process and
separately identify the related genes and pathways using SNP data. In this
study, we constructed a hierarchical component model that consists of
3-layers to represent the SNP-gene-pathway process. In this model,
pathways are defined as a weighted component of a set of genes, and genes
are defined as a weighted component of a set of SNPs. This model analyzes
all SNPs, genes and pathways simultaneously by ridge-type penalization of
the SNP, gene and pathway effects on the phenotype. Statistical significance
of the SNP, gene and pathway coefficients can be examined by permutation
tests. We applied our method to a SNP chip dataset of KARE for type 2
diabetes. The results showed that our method could successfully identify
signal pathways with superior statistical and biological significance. Our
approach has the advantage of providing an intuitive biological
interpretation for associations between common variants and phenotypes.
ICBRA 2019 CONFERENCE ABSTRACT
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K1023
Session 3
Presentation 4
(16:00-16:15)
Predicting Individual Risk of Malignancy in the Patients with Intraductal
Papillary Mucinous Neoplasms of the Pancreas using Automated Machine
Learning
Chanhee Lee, Hae Seung Kang, Jin-Young Jang, Taesung Park
Interdisciplinary Program in Bioinformatics, Seoul National University,
Seoul, Korea
Department of Surgery and Cancer Research Institute, Seoul National
University College of Medicine, Seoul, Korea
Department of Statistics, Seoul National University, Seoul, Korea
Abstract—Intraductal papillary mucinous neoplasms (IPMN) are
premalignant lesions of the pancreas. Although clinical guidelines were
released in 2012 to improve diagnosis, treatment for IPMN, due to
somewhat vague terminology and insufficient data, classifying IPMN and
assigning individual risks of malignancy to each patient remain unclear. To
evaluate individual risk of malignancy and to classify IPMN into benign or
malignant groups, we used large database of 3,464 patients from 31 different
hospitals, both Asian and Western cohorts. This study was a multi-national
(Korea, Japan, United States, China, Sweden, and Taiwan) retrospective
study. We then used automated machine learning to choose the best machine
learning algorithm for classifying IPMN patients. Most nomograms
predicting malignant intraductal papillary mucinous neoplasm (IPMN) of
pancreas were developed based on the logistic regression (LR) analysis. Six
algorithms of ML (XG boost, deep learning, distributed random forest,
generalized linear mode, gradient boosting machine, and stacked ensemble)
were utilized and compared. The algorithm which had the best performance
was selected. This study was to develop a nomogram using machine
learning (ML) and compare the performances between ML and LR model.
The performance using ML was valid in clinical circumstances
K1024
Session 3
Presentation 5
(16:15-16:30)
The Predictive Model using Extracellular Vesicles (EVs) Microbiome
Successfully Predict Matched Pancreatic Ductal Adenocarcinoma (PDAC)
and Non-cancerous Sample
Kyulhee Han, Nayeon Kang, Jae Ri Kim, Jin-Young Jang, Taesung Park
Interdisciplinary Program in Bioinformatics, Seoul National University,
Seoul, South Korea
Department of Surgery and Cancer Research Institute, Seoul National
University College of Medicine, 101 Daehak-ro, Chongno-gu, Seoul,
110-744 South Korea
Department of Statistics, Seoul National University, Seoul, South Korea
Abstract—Pancreatic Ductal Adenocarcinoma (PDAC), the most common
type of pancreatic cancer is one of the deadliest cancer that shows poor
prognosis. Most of PDAC patients are diagnosed their disease in advanced
stage, because most PDAC cases are asymptomatic in early stage.
Therefore, it is urgent that find the early detection method of PDAC to
ICBRA 2019 CONFERENCE ABSTRACT
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improve the overall survival of patients. Within many kind of possible target
– including genetic predisposition, toxic chemical, bacterial infection, and
etc. –, we hypothesized microbiome could be used in prediction of PDAC.
We used extracellular vesicles (EVs) metagenome data to select significant
markers and build a prediction model. Among the 87 PDAC (case) and 151
non-cancerous (control) samples, we selected 50 case and 67 control
samples using Propensity Score Matching (PSM). Several statistical
methods were applied to find differential abundance in both of phylum (L2)
and genus (L6) level. We found the 2 markers (Verrucomicrobia,
Actinobacteria) in phylum level, and 7 markers (Akkermansia,
Propionibacterium, Sphingomonas, Lactobacillus, etc.) in genus level. Our
prediction model shows higher auc than 0.8 both on phylum (0.813) and
genus level (0.879) in testing set. In conclusion, we suggest EVs
metagenomes could be used for early detection of PDAC patients.
K2015
Session 3
Presentation 6
(16:30-16:45)
DrugCell: A Visible Neural Network to Guide Precision Medicine
Kuenzi BM, Park J, Fong S, Ma J, Kreisberg JF and Ideker T,
University of California San Diego, USA
Abstract—The rate of successful translation from the bench to the bed has
not been satisfying. Many factors contribute to this problem but in most
cases, failure occurs due to the lack of understanding of how a cancer cell
responds to a particular drug. There has recently been a great deal of interest
in applying the staggering advances in artificial intelligence (AI), deep
learning in particular. However, deep learning-based models suffer from a
fundamental pitfall: these models lack interpretability as their internal
structures are ―invisible‖. To address this challenge, we developed a ―visible
neural network‖, which not only predicts anti-cancer drug response but also
allows for in silico predictions of the underlying molecular events driving
therapeutic responses. The interpretability of our prediction is achieved by
simulating the impact of genomic variations on cancer cells through an
embedded cancer cell hierarchy. Our findings are consistent with the
published synergistic drug pairs and the results of in-house CRISPR/Cas9
mediated knockout experiments. When applied to AML patient samples, our
model highlights pathways that include synergistic drug targets. The visible
AI paves the way for the next generation of intelligent systems in drug
discovery, contributing to the development of novel therapeutic solutions to
tumors.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 4
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 20, 2019 (Friday)
Time: 15:15-16:45
Venue: Room 105
Topic: “Biomedical Engineering”
Session Chair: To be Added
K4013
Session 4
Presentation 1
(15:15-15:30)
An EEG-based Depression Detection Method using Machine Learning
Model
Ran Bai, Yu Guo, Xianwu Tan, Lei Feng and Haiyong Xie
National Engineering Laboratory for Public Safety Risk Perception and
Control by Big Data (NEL-PSRPC), China
Abstract—Depression, different from usual mood fluctuations and
short-lived emotional responses to challenges in everyday life, is a common
illness worldwide, with more than 300 million people affected. Although
there are known, effective treatments for depression, fewer than half of
those affected in the world (in many countries, fewer than 10%) receive
such treatments. The diagnose of depression is usually subject to doctors due
to the lack of biomarkers of depression. Electroencephalogram (EEG) is an
easy-to-use, cost-effective technique that records electrical activity in brain.
In this study, 64-channel EEG data was collected from 213 subjects
including 71 health controls and 142 depression patients. 13 different
features were extracted from EEG signals from all 7 sub-bands of all
channels. 3 different feature selection models were used to find the subset of
features that best represents the characteristics of EEG signal and 6 machine
learning models were applied on all subsets of features to find the model
that gained the highest accuracy and recall on depression detection.
K4020
Session 4
Presentation 2
(15:30-15:45)
Identification of Raw EEG Signal for Prosthetic Hand Application
Azizi Miskon, Ayu Kusuma Sari Djonhari, Satria Mohd Haziq Azhar,
Suresh A/L Thanakodi and Siti Nooraya Mohd Tawil
National Defence University of Malaysia, Malaysia
Abstract—This paper presents the identification of raw
Electroencephalograph (EEG) signal for prosthetic hand application. The
main aim of this study to identify the EEG signal from human brain in real
time using Emotiv headset to control the prosthetic hand. Emotiv Epoc+
headset, Arduino Microcontroller and Prosthetic hand were the main
equipment used in this work. The prosthetic hand movement in this work
ICBRA 2019 CONFERENCE ABSTRACT
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subjected only for opening and closing hand operation. This paper focuses
on analyzing two different methodology of prosthetic hand controlling
technique which is using the hand movement and facial expression
technique. This study able to conclude that the raw EEG signal data
obtained from facial expression method using eye blinking technique shows
better performance in real-time for software and prosthetic hand integration
by generating signal voltage peak more than 5000 µV compared to the usage
of EEG data obtained from just hand movement technique.
K4024
Session 4
Presentation 3
(15:45-16:00)
Spatio-temporal Pattern Analysis for EEG Classification in Rapid Serial
Visual Presentation Task
Bowen Li, Zhiwen Liu, Xiaorong Gao and Yanfei Lin
Beijing Institute of Technology, China
Abstract—This study will explore an algorithm of spatio-temporal pattern
analysis for electroencephalographic (EEG) classification in the rapid serial
visual presentation (RSVP) task. In this algorithm, the spatial low-rank and
temporal-frequency sparse priors are exploited to train the supervised spatial
and temporal filters. The discriminant features are extracted by the
supervised spatio-temporal filters and classified by support vector machine.
The EEG signals were recorded from a total of 12 subjects under RSVP task
and were used as training and testing data. The average true positive rate of
classification is 79%, and the average false positive rate is only 3.4%. The
classification results show that the proposed algorithm has better
performance in the target detection than HDCA and SWFP.
K0011
Session 4
Presentation 4
(16:00-16:15)
Development of Arduino Microcontroller-based Safety Monitoring
Prototype in the Hard Hat
Robert D. Arcayena Jr, Alessis D. Ballarta, Kendall N.Claros and Rodrigo S.
Pangantihon Jr.
University of Mindanao, Philippines
Abstract—Construction, being one of the most dangerous sectors in the
industry, comes with fortuitous or inevitable accidents. Occupational
injuries like falling from heights, being hit by falling or moving objects,
fatigue related complications, and heat induced illnesses cause construction
losses. Despite common safety protocols, construction workers still have a
chance of 1-in-200 of dying on the job within the span of a 45- year career
making safety an issue of paramount importance for construction contractors
to monitor and manage. The main objective of this study is to ergonomically
design a hard hat with biometric sensors, an accelerometer, a GPS module,
transceivers, a fingerprint scanner, and an emergency button, all connected
to Arduino Uno Microcontroller. It monitors the biometrics of the worker,
detect external impact forces, know the location, and send distress alert
signals during emergencies. By creating a software that presents the
wearer‘s profile with the gathered data, information generated are verified
through secondary equipment for necessary calibrations. The prototype was
ICBRA 2019 CONFERENCE ABSTRACT
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ergonomically designed with a reliable overall performance. Pulse and
temperature monitoring acquired overall accuracies of 95.62% and 99.36%,
distress alerting with 95%, impact detection, location identification, and
fingerprint scanning got 100% through performance analysis.
K4009
Session 4
Presentation 5
(16:15-16:30)
Improvement of the BT-Heartomotive Device for Avert Car Accident using
MYBradyTachyHeart Mobile Application
Mohd Azrul Hisham Mohd Adib, Muhammad Irfan Abdul Jalal and Nur
Hazreen Mohd Hasni
Universiti Malaysia Pahang, Malaysia
Abstract—Nowadays, the pulse oximeter used in the medical device is a
non-invasive sensor capable for monitoring the blood's oxygen saturation. It
has been widely used in medical, fitness and clinical care. The prototype
development of brady-tachy heart automotive so-called BT-Heartomotive
device is well developed. This device purposely to prevent motor vehicle
accident using the oximeter sensor. In this study, we focus on enhancing the
BT-Heartomotive device to preventing the car accident by using a mobile
application. The emergence of wearable sensor and wireless mobile
technologies enable to detect and monitor the changes in health parameters
irrespective of places and time. It will be much more convenient for the
patient to do a self-test diagnosis by using a wireless heart monitoring
device. The BT-Heartomotive device is simple, easy to use, low cost,
automated and provides reliable heart rate monitoring result. This kind of
real-time assistive medical diagnosis system consists of a pulse oximeter
sensor. The heart disease can be detected if the threshold value of the heart
rate is maximally exceeded. The pulse sensor and mobile apps. is connected
wirelessly via Bluetooth module. Then, the pulse sensors used for
transmitting the heart rate signals to the mobile apps. and monitor device.
These mobile apps. used for monitoring purpose to display the patient‘s
heart rhythms on the screen of the phone. The driver can observe their heart
rhythms easily by using this mobile app. This device also alerts the
passenger to quickly attend to help the driver. The device shows good
accuracy in the detection of the heart rate level. Heart rate measurement can
reveal a lot about the physical conditions of an individual.
K1004
Session 4
Presentation 6
(16:30-16:45)
Contributions of Novel Nanomaterials to Pharmaceutical Analysis
Yixin Zhang
The Taft School, USA
Abstract—This paper is an analysis of the application of nanomaterials in
pharmaceutical situations. The paper discusses the contribution of
nanomaterials in liquid chromatography - mass spectrometry (LC-MS),
capillary electrophoresis (CE), and chiral separation. Numerous researches
demonstrate the positive impact of different types of nanomaterials in a wide
range of areas, thus showing their promising future for medical purposes.
Moreover, some researches mentioned in this paper are the first reported
ICBRA 2019 CONFERENCE ABSTRACT
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cases of their areas, indicating that more research needs to be done in order
to prove the stability of nanomaterials in certain situations. Overall, the
combination of nanotechnology and medical analysis is very efficient, which
means this field is highly valuable for further study.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 5
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 20, 2019 (Friday)
Time: 17:00-18:30
Venue: International Meeting Room
Topic: “Bioinformatics”
Session Chair: Assoc. Prof. Yoichi Murakami
K4014
Session 5
Presentation 1
(17:00-17:15)
Automated SNOMED CT Mapping of Clinical Discharge Summary Data
for Cardiology Queries in Clinical Facilities
Abdul Aziz Latip, Ma. Stella Tabora Domingo, 'Ismat Mohd Sulaiman and
Tengku Nurulhuda Tengku Abd Rahim
MIMOS, Malaysia
Abstract—Heart disease has remained the leading cause of death among
Malaysians for 13 years from 2005 to 2017 [1]. As it has become the
prominent factor of death in Malaysia, the intention is to improve the
accuracy of query for cardiology related cases as it is the primary source of
analytical data for heart disease. Choosing the right terminology is one of
the criteria to improve the accuracy as the clinical term can be mapped as
much as possible. Therefore, Systematized Nomenclature of Medicine
Clinical Term (SNOMED CT) has been selected for implementation as it is
known as the most comprehensive, multilingual clinical healthcare
terminology in the world. This paper presents the implementation to enrich
and increase the result accuracy by automatically mapping the Clinical
Discharge Summary using several techniques in Natural Language
Processing (NLP) with SNOMED CT. By observing the trend and pattern of
data, a facility or ministry can plan one step ahead, through prevention or
future planning. Therefore, the accuracy of the result is the key factor to
derive the outcome.
K4008
Session 5
Presentation 2
(17:15-17:30)
Acceptability of Virtual Reality among Older People: Ordinal Logistic
Regression Study from Taiwan
Diana Barsasella, Shankari Priya Chakkaravarthi, Hee-Jung Chung, Mina
Hur, Shabbir Syed Abdul, Shwetambara Malwade, Chia-Chi Chang, Megan
F. Liu and Yu-Chuan Li,
Taipei Medical University, Taiwan
Abstract—By 2050, it is estimated that 80% of older people will be living in
low- and middle-income countries. The older people are seen to limit their
physical activity after the age of 65 years. The aim of this study was to
explore the influence of sex and age towards active ageing to answer and
ICBRA 2019 CONFERENCE ABSTRACT
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agree a usability and acceptance of Virtual Reality (VR). This pilot study
involved 30 older people who voluntarily participated in March to May
2018. They were asked to use VR for 15 minutes twice a week for 6 weeks.
We used ordinal logistic regression to see whether sex and age influenced
the answer in the Technology Acceptance Model (TAM) questionnaire.
SPSS vers.21 was used to perform statistical analyses of the data. We found
that most of them have agreed to the acceptance of VR use for each
variables of according to the sex and age.
K1010
Session 5
Presentation 3
(17:30-17:45)
Identification of Key Genes Associated with Kidney Cancer Through
Pan-cancer Bioinformatics Analysis
Nur Ain Rodzi and Suresh Kumar
Management & Science University, Malaysia
Abstract—Kidney Cancer is also known as Renal Cell Carcinoma (RCC) is
the most common and most lethal renal malignant tumour in adults. RCC
incidence levels have been reported to increase in both men and women.
Differentially expressed genes associated with kidney cancer were obtained
from the HIVE Lab (High-performance Integrated Virtual Environment)
Database were analysed. Key genes related to the pathogenesis and
prognosis of RCC were identified by employing protein–protein interaction
network. We identified ten hub genes of upregulated (EDN1, CDC25C,
P30273, LPAR5, SNAP25, GBP1, CTLA4, PECAM1) and downregulated
(PVALB, PRL, FAIM2, ATP12A, FSHB, TAC1, PENK, AFP, KCNJ9,
OCLN) of differentially expressed genes. The gene enrichment reveals
upregulated genes involved in positive regulation of renal sodium excretion,
cell surface receptor signaling pathway,plasma membrane, hormone activity
and pathway invovled in G alpha (q) signalling events. The down-regulated
genes involved in potassium ion import, neuropeptide signaling and
pathway invovled in Cell adhesion molecules and Leukocyte
transendothelial migration. The findings of this study would provide some
directive significance for further investigating the diagnostic and prognostic
biomarkers to facilitate the molecular targeting therapy of RCC.
K0008
Session 5
Presentation 4
(17:45-18:00)
Visualization of Differential Arm-specific miRNA Expression with TCGA
Dataset
Chao-Yu Pan and Wen-Chang Lin
Academia Sinica, Taiwan
Abstract—microRNAs play important regulatory roles in cellular functions
and developmental processes. They are also implicated in human
oncogenesis processes and could serve as potential cancer biomarkers. We
have been working on the discovery of miRNAs using computational
pipelines as well as NGS sequencing data. In previous studies, we
established comprehensive 5p-arm and 3p-arm miRNA annotations and
applied them for thorough interrogation on the arm-specific miRNA cancer
expression profiles. We utilized The Cancer Genome Atlas (TCGA) miRNA
ICBRA 2019 CONFERENCE ABSTRACT
- 51 -
expression datasets and explored the 5p-arm / 3p-arm miRNAs differential
expression patterns. Following statistical analysis, differentially expressed
5p-arm / 3p-arm miRNAs could be identified in various cancer types. We
identified several miRNAs significantly modulated in each cancer types.
This implicated the significance of these miRNAs in the oncogenesis
processes and could server as universal human cancer biomarkers. We then
established interactive web resource to assist biologists exploring the unique
expression profiles of individual miRNAs in different cancer types. Our goal
is to better visualize the miRNA expressions using visual analytics
techniques mainly based on the D3 JavaScript libraries. By using advanced
interactive visual user interface, our web tools could allow users to learn
more about multidimensional miRNA expression data in TCGA.
K0030
Session 5
Presentation 5
(18:00-18:15)
The Method of Organizing a Service-oriented User Interface for Multi-agent
Information and Control Systems
Iakov S. Korovin, Donat Ya. Ivanov and Sergei A. Semenistyi
Southern Federal University, Russia
Abstract—In this paper we describe a proposed method for organizing a
service-oriented interface for multi-agent information and control systems,
focused on solving large-scale problems, based on distributed computing.
We dwell on the detailed description of the architecture and elements of the
user interface, based on the structure of the solved task parameters. The
translation of these descriptions into a graphical representation within the
framework of a dynamically generated visual shell of the user is proposed.
K0031
Session 5
Presentation 6
(18:15-18:30)
Implementation of Fingerprint Recognition using Convolutional Neural
Network and RFID Authentication Protocol on Attendance Machine
Maredi Aritonang, Irwan Doni Hutahaean, Hasudungan Sipayung and Indra
Hartarto Tambunan
Institut Teknologi Del, Indonesia
Abstract—The attendance machine is a machine that can record a person's
attendance data at an institution or office that applies it. The current
attendance system is considered to be less effective because it still
implements a manual system that has weaknesses in its use, such as many
paper usage and opening gaps to falsify data. One effort to solve this
problem is to use fingerprint and RFID attendance machine. In this research,
the fingerprint grouping is performed, so the counterfeit presence data can
be minimized due to the unique and identical fingerprint pattern. The
process of grouping fingerprint images requires an approach that uses the
convolutional Neural Network (CNN) algorithm because of the difficulty of
distinguishing fingerprint patterns. Based on the results of the
implementation, it has managed to obtain an accuracy rate of 99.64%,
validation accuracy of 99.83%, and a loss of 0.001% against 2 image
classes. The attendance machine also uses RFID technology as an
alternative if the fingerprint system is experiencing interference. In this
ICBRA 2019 CONFERENCE ABSTRACT
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research, the RFID authentication process uses the RC4 (Rivest Code 4)
cryptographic algorithm to encrypt the Unique Identifier (UID) of the
student card. Attendance machine built using Raspberry Pi 3 microcontroller
integrated with the GT-521F52 type fingerprint sensor and RFID RC522.
The system saves the recorded data in the database and connects to server so
that it can be accessed by the user via the website.
ICBRA 2019 CONFERENCE ABSTRACT
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Session 6
Tips: The schedule for each presentation is for reference only. In order not to miss your presentation,
we strongly suggest that you attend the whole session.
Afternoon, December 20, 2019 (Friday)
Time: 17:00-18:30
Venue: Room 105
Topic: “Image Analysis”
Session Chair: Assoc. Prof. Azizi Miskon
K0003
Session 6
Presentation 1
(17:00-17:15)
Identification and Classification of Export Quality Carabao Mangoes
Johannie Ave P. Ardepolla, Mike Jhon Reymar Cortez, Abigail L. Escorpion,
Jetron J. Adtoo and Kimberly M. Nepa
University of Mindanao, Philippines
Abstract—Mangifera Indica or most locally known as carabao mango is the
most commonly used variety being used for case sampling in the evaluation
of automatic mango grading system. As usually practiced, the quality of
mango is being assessed by its physical look and texture. Nowadays, the
utilization of scientific strategy for quality evaluation of mango is done
through image processing and machine learning, which is more efficient,
non-destructive and cost-effective grading method. Classified sample
carabao mangoes from a mango export Company were analyzed and
become data sets of the device. Carabao Mangoes are classified to be
Export Quality, Reject Quality and Unknown. In this paper, proposed
methodology is divided into three parts namely: (i) identifying the color of
the mangoes through RGB color recognition, (ii) grading of mango based on
its weight, (iii) determining the size of the mango by its height and width.
Functionality test and statistical analysis revealed 90 percent overall
accuracy of the device.
K0009
Session 6
Presentation 2
(17:15-17:30)
A Supervised Learning Approach on Rice Variety Classification using
Convolutional Neural Networks
Louie John L. Castillo, Juvy Amor M. Galindo and Jamie Eduardo C.
Rosal
Cor Jesu College, Philippines
Abstract—This project is about developing a portable imaging system using
Raspberry Pi that can obtain rice grain visual parameters datasets for Image
Processing. The system aims to identify and classify at least three (3)
Specific Rice varieties; use supervised learning approach on Convolutional
Neural Networks (CNN) to automatically attain the results in real-time.
CNN is a powerful algorithm to classify images. CNN was used preferably,
since other Artificial Neural Networks types needs to obtain several
ICBRA 2019 CONFERENCE ABSTRACT
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numerical parameters to be trained, and relatively require more human
efforts. The accuracy of the developed system for novel rice grain samples
have been tested for above 90% percent. The device captures the rice image
using a Raspberry Pi Camera, the captured image are then processed. 500
individual images of rice grains per variety are trained in the CNN model
and 50 epochs were used to ensure better accuracy. Apart from the 3 tested
varieties, new varieties can still be trained and tested in the CNN. The
device can assess physical and visual features of the rice. Other features
such as chemical and genetic traits are not detectable in the system.
Philippine local government does not a better materials to properly ensure
and the authenticity of the rice varieties sold in sold in the local market. This
device can help classify rice grains since there are no currently no easy
method and inexpensive tools for easily and conclusively classifying rice
grains because of the subtle its differences.
K0010
Session 6
Presentation 3
(17:30-17:45)
De-husked Coconut Quality Evaluation using Image Processing and
Machine Learning Techniques
Tito C. Lim Jr., Jaedy O. Torregosa, Aubrey Rose A. Pescadero and Rodrigo
S. Pangantihon Jr.
University of Mindanao, Philippines
Abstract—Qualitative evaluation provides the basis for determining if the
quality of products meets the target specifications. Manual evaluation of
de-husked coconuts is still being performed by coconut farmers, however, it
is time consuming and costly. Ergo this study aiming to replace the manual
inspection, a prototype was developed for objective and automated quality
evaluation of de-husked coconuts through the application of computer vision
and machine learning, identifying good-quality de-husked coconuts from
defective ones with respect to its RGB color space. JavaFX platform was
utilized to create the system performing K-Nearest Neighbor and Arduino
technology played a significant role in the hardware control of the device.
The image samples were captured by a CMOS camera in an imaging
chamber with invariant illumination on top of a conveyor belt. Image
processing is done to get the required features of the sample and by
comparing the average RGB value from the custom dataset, then the
maturity level of the coconut is determined. With the accuracy of 86.667%,
the system is able to evaluate de-husked coconuts which are good for further
processing used in export and premature coconuts that are to be rejected.
K0018
Session 6
Presentation 4
(17:45-18:00)
Data Mining of Daily Pig Behaviors using Wireless IC Tag based
Monitoring System in Pig Farms
Geunho Lee, Atsushi Ishimoto, Shinsuke H. Sakamoto and Seiji Ieiri
University of Miyazaki, Japan
Abstract—Automatic sorting systems (afterward, auto sorter) used in pig
farms have been mainly manufactured and used in European countries and
America. However, from the viewpoint of pig welfare and growth
ICBRA 2019 CONFERENCE ABSTRACT
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performance, no scientific information exists about the auto sorters to direct
stockbreeders. As considering these situations, our research aims to
investigate any influences on daily pig behaviors caused by the auto sorter
used in the pig farm. Under this research direction, our paper tackles what
kind of a sensor will be used and how to collect biological data for the pigs
toward the application to a large pig farm. As a solution approach, our study
proposes a monitoring system that uses the relative received signal strength
transmitted from IC tags attached to individual pigs, enabling the system to
obtain the behavioral data such as dwell time at feeding or resting areas,
amount of movement, and so on. The implementation of the monitoring
system are explained in detail, and its effectiveness and usability are verified
through field experiments. Finally, our future work includes accessing to
information from any locations and obtaining not only behavioral
information but other biological information.
K0002
Session 6
Presentation 5
(18:00-18:15)
Supervised Machine Learning Approach for Pork Meat Freshness
Identification
Christell Faith D. Lumogdang, Christell Faith D. Lumogdang, Stephone Jone
S. Loyola, Randy E. Angelia and Hanna Leah P. Angelia
University of Mindanao, Philippines
Abstract— As the number of pork consumer increases in the meat industry,
the demand for meat supplies also rises. Determining pork meat freshness,
therefore, is the primary consideration of the pork meat customers. This
smart study is mainly designed to assess and classify pork meat quality. Loin
parts weighing 100 grams from various pigs in the wet market, were
examined and became the data sets of the study, provided that a city
veterinarian has inspected and approved it. Photos of pork meat are captured
to undergo image processing. Simultaneously, electronic noses, specifically
MQ-135 and MQ-136, evaluated Ammonia and Hydrogen Sulfide
components of the pork meat, respectively. These parameters are then
classified using the k-Nearest Neighbor Algorithm. Pork meat is
distinguished from being fresh, half-fresh, and adulterated. By using the
confusion matrix principle, functionality test and statistical analysis revealed
that the system has a high accuracy rate of 93.33%.
K0004
Session 6
Presentation 6
(18:15-18:30)
Automated Vermiculture Monitoring and Compost Segregating System
using Microcontrollers
Menkent S. Barcelon, Alvin A. Orilla, Jessabelle A. Mahilum and Jetron J.
Adtoon
University of Mindanao, Philippines
Abstract—Vermicomposting is the process of breaking down biodegradable
matter by earthworms to convert the contained nutrients in the organic
matter to vermicast. The study was presented by the authors to introduce the
automation system of vermiculture. By the span of 14 and 16 days only, the
conducted experiment still produced acceptable nutrient and compost
ICBRA 2019 CONFERENCE ABSTRACT
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quality for a fertilizer. Sensor readings with water sprinkler system
combination do maintain the right environment for the living conditions of
the worm. Through the use of microcontrollers Arduino and Raspberry Pi,
human intervention can be lessened and the system would be expedited if
the process of vermicomposting will be automated rather than going manual.
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Poster Session Afternoon, December 20, 2019(Friday)
Time: 15:00-15:15&16:45-17:00
Venue: Lobby of Building 25-1
K4010
Poster 1
Discrimination Colonies of Staphylococcus Aureus and Salmonella Enterica
by using Machine Learning
Manao Bunkum and SarinpornVisitsattapongse
King Mongkut‘s Institute of Technology Ladkrabang Bangkok, Thailand
Abstract—Discriminative bacteria is very important because bacteria can
contaminate in food and environment. The bacteria are the cause of some
diseases. In the present, the technique for discrimination and counting
bacteria use a lot of time and budget. Moreover, the discrimination method
has done by human who expert in that way. So, this research will create
algorithm for solve these problems by use bacteria Staphylococcus aureus
and Salmonella enterica to pilot study. The bacteria samples must be show
single colony for good detection so, this research use stab technique for each
bacterium on Luria-Bertani agar (LB agar) plates. The image segmentation
technique was used to separate colony of each bacteria for train in machine
learning algorithm. This research use sample of bacteria‘s image around 800
images for training. This algorithm can count colony of bacteria at the
accuracy of 98.75 % and discriminate Staphylococcus aureus and
Salmonella enterica at the accuracy of 98.12%.
K5002
Poster 2
The Noninvasive Blood Glucose Monitoring by Means of Near Infrared
Sensors
Jindapa Nampeng, Yanisa Samona, Chuchart Pintavirooj, Baorong Ni and
Sarinporn Visitsattapongse
King Mongkut‘s Institute of Technology Ladkrabang Bangkok, Thailand
Abstract—Diabetes is a type of metabolic disease that causes a high blood
glucose level that wildly found in many countries. Blood glucose
measurement is necessary for diabetes patients to check how much glucose
is present in the blood. The typical method to measure blood glucose level is
an invasive method that gives a highly accurate result, but the patients get
suffer from physical pains and it has a higher risk of infection. This research
presents an alternative method, which is noninvasive blood glucose
monitoring by means of Near infrared sensors based on 940nm near infrared
spectrum and an artificial neural network analysis. The concept is focusing
on glucose absorbance detection when the spectrum passes through the
patient‘s finger. In processing the signals, the wavelet‘s transformation is
ICBRA 2019 CONFERENCE ABSTRACT
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selected to do signal conditioning and extract four eigenvalues. The four
eigenvalues are the key features for training the artificial neural network
analysis model that gives an efficiency prediction algorithm of blood
glucose level. The experiment shows that the accuracy of the noninvasive
method that has the approximate regression value is 0.9534. The
noninvasive blood glucose monitoring by means of Near infrared sensors
causes less pain and lower risk of infection when compared with the
invasive method.
K4018
Poster 3
In Vivo Performance and Biocompatibility of an Intelligent Artificial Anal
Sphincter System
Ding Han, Guo-Zheng Yan and Kai Zhao
Shanghai Jiaotong University, China
Abstract—Severe fecal incontinence is an embarrassing and psychosocially
debilitating condition that has a considerable negative impact on quality of
the life. This article describes an intelligent artificial anal sphincter system
(AASS) based on enteric cavity pressure signal feedback mechanism and its
in vivo experiment in two dogs. The optimized AASS consists of an external
telemetry unit, internal artificial anal sphincter (IAAS) and transcutaneous
energy transfer charging system (TETCS). The new sphincter prosthesis was
designed with pressure sensor to simulate the part function of the external
anal sphincter. The devices were implanted in two dogs and studied for
periods of up to 5 weeks. The efficacy of the device in achieving continence
and sensing the stool was assessed. The biocompatibility and biosecurity,
including blood supply of the rectum, blood serum chemistry, and histologic
examination of tissue, were evaluated during and after experiment. Results
of the chronic animal experiment demonstrated no significant tissue
inflammation. Functionality and biocompatibility of the improved device
have been proved.
K4022
Poster 4
Optimization of the Treatment of Chronic Eczema in the Elderly
Zhumash Nurmukhambetov, Torgyn Ibrayeva, Alibek Nurmukhambetov
and Yerlan Bazarbekov
Semey Medical University, Kazakhstan
Abstract—The high prevalence and social significance of eczema in the
modern world are not in doubt. According to various authors, it takes from
10% to 40% of all cases of skin diseases [1, 2, 3]. However, in the elderly,
eczema accounts for even more than 50% in the structure of skin pathology
[4]. This problem is of extreme importance in the modern world, given the
fact that, at the moment, the whole world is focused on a significant increase
in the number of the elderly. In this sense, the need for dermatological care
in people of this category is significantly higher than in people of working
age. Many treatment methods have been developed, but medical practice
urgently requires the improvement and creation of more effective therapies.
ICBRA 2019 CONFERENCE ABSTRACT
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K4025
Poster 5
The Efficacy and Safety of Long-term Aspirin Use for Cancer Primary
Prevention: An Updated Systematic Review and Subgroup Meta-analysis of
Randomized Controlled Trials
Qibiao Wu, Xiaojun Yao, Hongwei Chen and Elaine Lai-Han Leung
Macau University of Science and Technology, China
Abstract—Long-term aspirin use for primary prevention of cancer remains
controversial, and variations in the effect of aspirin use on cancer outcomes
by aspirin dose, follow-up duration, or study population have never been
systematically evaluated. This updated meta-analysis was conducted to
evaluate the efficacy and safety of aspirin use for cancer primary prevention
and determine whether the effect differed according to aspirin dose,
follow-up duration, or study population.
Seven electronic databases (PubMed, EMBASE, ClinicalTrials.gov, etc.)
were searched from inception to September 30, 2019. Randomized clinical
trials (RCTs) comparing aspirin use versus no aspirin use in participants
without pre-existing cancer that reported cancer incidence and/or cancer
mortality outcomes were selected and assessed for inclusion. Studies with a
follow-up of at least one year were eligible. Data were screened and
extracted by different investigators. The Cochrane‘s Risk of Bias Tool and
the Jadad scale were used to evaluate the risk of bias and the methodologic
quality of the RCTs. Analyses were performed using Review Manager 5.3,
Comprehensive Meta-Analysis 2.0 and Trial Sequential Analysis software
(TSA). The Grading of Recommendations Assessment, Development and
Evaluation (GRADE) working group methodology was used to assess the
strength of the body of evidence. Total cancer incidence was defined as the
primary clinical endpoint. Total cancer mortality, all-cause mortality, major
bleeding, and total bleeding events were the secondary outcomes. Subgroup
analyses were conducted based on aspirin dose, follow-up duration, and
study populations. 29 RCTs that randomized 200,679 participants were
included. Compared with no aspirin, aspirin use was not associated with
significant reductions in total cancer incidence (RR = 1.01, 95% CI 0.97 to
1.04, P = 0.72), total cancer mortality (RR = 1.00, 95% CI 0.93 to 1.07, P =
0.90), or all-cause mortality (RR = 0.98, 95% CI 0.94 to 1.02, P =0.31);
however, aspirin use was associated with a 44% increase in the risk of major
bleeding (RR = 1.44, 95% CI 1.32 to 1.57, P < 0.00001) and a 52% increase
in the risk of total bleeding events (RR = 1.52, 95% CI 1.33 to 1.74, P <
0.00001). The results were consistent when subgroup analyses were
performed based on the daily dose of aspirin, follow-up duration, and study
population. Trial sequential analysis and the meta-regression analysis
indicated that aspirin use was not significantly superior to no aspirin use,
and the total cancer incidence, total cancer mortality and all-cause mortality
rates were not reduced with a larger daily dose of aspirin or a longer
follow-up duration. Most results were robust, and the quality of evidence
ranged from moderate to high. Long-term use of aspirin in individuals
ICBRA 2019 CONFERENCE ABSTRACT
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without pre-existing cancer was not associated with a significant reduction
in total cancer incidence, cancer mortality, or all-cause mortality; however,
aspirin use was associated with a significant increase in the risk of bleeding.
Therefore, aspirin is not an appropriate choice for primary prevention of
cancer. Prospective clinical trials are warranted.
K1005
Poster 6
Rational Design of NOT-gate in Tri-node Enzyme Regulatory Networks
Xiao Wang and Xudong Lv
Shandong University at Weihai, China
Abstract— Synthetic biology shows a lot potential building biological
systems to perform various target function using basic bio-circuits as
modules. However, the design of NOT-gate, as a critical circuit component
in enzyme regulation network, has been rarely attempted. We
computationally searched all possible tri-node enzyme network topologies
to identify those who could present NOT-gate behavior. The results show
that a NOT-gate can be achieved if and only if the network contains a
(Direct or Indirect) Negative Feedforward link from Input to Output
(DNFIO/INFIO). Furthermore, we discovered a negative feedback on input
node improves NOT-gate performance. The minimal DNFIO motif was
analytically interpreted, matching with our computational results. This study
adds a curial component to the design toolbox of synthetic biology and
paves the way for deeper understanding negative feedback systems such as
blood glucose regulation.
K2007
Poster 7
Genetic Mutations Associated with Diffuse Large B-cell Lymphoma
Jinghan Qiu
Rutgers Preparatory School, USA
Abstract—Diffuse Large B-Cell Lymphoma (DLBCL) is the most common
non-Hodgkin lymphoma (NHL) among adults. [1] A cancer of B cells,
DLBCL can arise in any part of the body. [2] Although DLBCL is not well
understood and classified right now, substantial credible clinical data took
by authoritative organizations are available online. Online clinical data
related to DLBCL include data of mutated gene, high sequence, gene
expression, copy number, etc. Here, utilizing clinical data, the researcher
finds the most frequently mutated genes, and by analyzing the
llluminaHiSeq in TCGA DLBCL data set, the researcher finds eleven
frequently mutated genes that have significant effect on certain genes‘
expression once mutated (ARID1A, HIST1H1E, MGA, ATM, SGK1, IRF8,
TET2, BTG2, EP300, CHD8, MLL2). If these eleven genes‘ mutation can
be controlled by medical therapies, patients with DLBCL may be treated
because of the reducing irregular gene expressions.
K4006
Poster 8
Comparison of Two Different Kernel Functions of Support Vector
Regression for Tracking Tumor Motion: Radial Basis Function and Linear
Function
Jie Zhang, Xue Bai and Guoping Shan
ICBRA 2019 CONFERENCE ABSTRACT
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Zhejiang Cancer Hospital, China
Abstract—tumor between two different kernels of support vector regression
(SVR). The two kernels are radial basis function (RBF) and linear function.
Methods: The comparison focused on the prediction accuracy. A RBF-based
SVR (RBF-SVR) and a linear function based SVR (Linear-SVR) were both
applied on the same bi-modal liver motion data. The data were shared on a
website. It involved 15 sets of a vessel bifurcation‘s motion and three
external skin markers‘ motion. The vessel bifurcation‘s motion was regarded
as target motion in our work. All signals were 6~20 minutes in length. To
simulate the modeling phase and predicting phase in real applications, the
first 5-minute session was used to build and train the model. The rest was
used for validation. Results: For RBF-SVR, 80% cases had a prediction
error of less than3.7mm; 90% cases had a prediction error of less than
5.6mm. Linear-SVR achieved a prediction error of less than 2.8mm for 80%
cases and a prediction error of less than 3.5mm for 90% cases. Besides,
Linear-SVR had a better root-mean-square error than RBF-SVR.
Conclusion: To predict a moving target position using the higher-dimension
traces of external skin markers, Linear-SVR can achieve a better accuracy
than RBF-SVR.
K4007
Poster 9
The Accuracy Heart Dosimetric Study of Left-breast Cancer Radio-therapy
using Deformable Image Registration
Xue Bai, Shengye Wang, Binbing Wang and Jie Zhang
Zhejiang Cancer Hospital, China
Abstract—The radiation injury of heart is an obviously risk in left-breast
cancer radiotherapy. In this study, the uncertainty of intra-fraction and
inter-fraction for heart dose was investigated using the 4DCT, CBCT and
deformable image registration (DIR) to understand the exact dose. The
secondary objective of this study was to evaluate the impact of DIR
uncertainty on dose accumulation. 4DCT and CBCT images were scanned
for ten left-breast cancer patients before and during 3D-CRT treatment. An
anatomically constrained hybrid DIR method and a biomechanical model
based DIR method were applied to dose accumulation. Dose scenarios
included plan dose (no motion), 4D dose (intra-fraction motion) and
accumulated dose (inter-fraction motion). The doses to the heart were
assessed. the differences among plan heart dose, 4D heart dose and
accumulated dose of the investigated parameters Dmean, Dmax, V10, V20, V30
and V40 were ranged -3.8~7.1%, -0.6~-0.5%, -1.3~0.8%, -0.8~1.7%,
-0.8~1.7% and -0.5~2.2% respectively. The uncertainty between the two
DIR methods were ranged -1.3~0.2% for all the investigated parameters of
heart. There was minimal uncertainty of cardiac dose in DIR algorithms,
meanwhile the intra-fraction variation was lager, and the inter-fraction
variation was the largest uncertainty.
ICBRA 2019 CONFERENCE ABSTRACT
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K4016
Poster 10
Druggability of Intrinsically Disordered Proteins and Their Virtual
Screening Strategy
Yutong Wan
New Jersey Institute of Technology, USA
Abstract—Intrinsically disordered proteins (IDPs) widely exist in nature and
have important physiological functionalities in life. They are related to
multiple human diseases. Thus, studying IDPs provides new opportunities
on drug design. This paper briefly reviewed the research progress of IDPs.
Then a new approach was introduced which utilised a software called
CAVITY to seek potentially druggable cavities in IDPs and analysed the
structural conservation of these cavities. We discover that even if IDPs lack
of stable secondary or tertiary structure, the structures of potentially
druggable cavity are still able to maintain a good consistency. Finally, this
paper discussed the possibilities of IDPs being drug design targets and the
rational strategy of drug design on IDPs.
K4019
Poster 11
Multiple Absorption Spectra Modeling Method for Improving Model
Stability in Spectral Analysis
Yongshun Luo, Gang Li and Ling Lin
Tianjin University, China
Abstract—In spectral quantitative analysis, the stability of the model
determines its application value. The stability is affected by the difference
between modeling conditions and application conditions. A multiple
absorption spectra modeling method (MASM method) for a weak scattering
solution is proposed in this study. The influence of external measurement
conditions on the robustness is illustrated by the difference in incident lights
caused by changing the positions of the light source. The MASM method
can suppress these effects and maintain a high prediction accuracy. In this
paper, a verification experiment is designed. The light source is accurately
located at three equidistant positions and the transmission spectra is
measured at four positions with equal spacing. The single absorption
spectrum modeling method (SASM method) and the MASM method are
used for modeling and analysis respectively. The results show that the
prediction accuracy of the MASM method is 40%-81.2% higher than that of
the SASM method, which proves that the MASM method has strong
robustness towards the changes in incident light intensity.
ICBRA 2019 CONFERENCE ABSTRACT
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Note
ICBRA 2019 CONFERENCE ABSTRACT
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Note