How to make a presentation

(Oral and Poster)

Dr. Bernard Chen Ph.D.University of Central Arkansas

July 5th

HIT@UCA Applied Research in Healthy Information

Outline

Presentation Overview Poster Presentation Oral Presentation My presentation in conference

Important things to look for conferences

CFP: call for papersExample: http://www.cs.gsu.edu/BIBM2011/?q=node/6

Relevant Topics

Important Dates

Presentation Overview

It all start with:

Presentation Overview Accepted paper type:

Oral Presentation (usually 15~20 minutes presentation, 5 minutes for questions)

Regular research paper Short research paper

Poster Presentation Poster paper

Example: http://acmbcb.org/accepted-regular-papers/

Presentation Overview

Dress Code: Business Casual

Not necessary wear a suit Shirt, pant, with a tie would be

perfect

Outline

Presentation Overview Poster Presentation Oral Presentation My presentation in conference

Poster Presentation

So what then makes for an effective poster?

First of all “title” Title is the most important thing to

attract audience

Do NOT typeset the title in all capital letters (Hard to read)

Put key words in Title

Second “the purpose”

“10 seconds” is about the time that a person can spend to recognize the work

Clearly define the purpose of the paper

The type is large enough to read

Third, “sections” Clearly separate each section

Introduction (This part should include the main research purpose)

Methods Results Conclusion

Not everyone will read all sections

Fourth, easy reading sections

the poster should NOT contain large blocks of text.

Nor the long sentences

Making Poster

Here is one poster template in power point format

Use “file => page setup” to change the size of your poster

Outline

Presentation Overview Poster Presentation Oral Presentation My presentation in an actual

conference

Oral Presentation

Understand the background of your audience

Oral Presentation Presentation style:

Never read word to word in your slides

Short sentences in your slides

Eye contact

Enthusiastic in your presentation

Oral Presentation

Contents: Most important three pages:

First page: Title page + introduction Second page: Outlines Last page: Thank you and Question page

Oral Presentation

Contents: Main Presentation Body

The main purpose of your research Methods Data Results Conclusion and future works

Oral Presentation

Practice makes it perfect Finish the presentation slides two

weeks before the D-day Rehearse at least two times with your

advisor Practice at least once/day, start one

week before the D-day

Oral Presentation

Arrive the room at least 15 minutes prior to the start of the session

Bring your laptop is always safe

Make two copy of your presentation in your jump drive and in your email

Outline

Presentation Overview Poster Presentation Oral Presentation My presentation in conference

Clustering on Protein Sequence Motifs using SCAN

and Positional Association Rule Algorithms

Dr. Bernard Chen Ph.D.Assistant Professor

Department of Computer ScienceUniversity of Central Arkansas

USA

July 18-21, Las Vegas, NV

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Protein Primary, Secondary, and Tertiary Structure

Protein Sequence Motif Although there are 20 amino acids, the

construction of protein primary structure is not randomly choose among those amino acids

Sequence Motif: A relatively small number of functionally

or structurally conserved sequence patterns that occurs repeatedly in a group of related proteins.

Goal of the our group The main purpose is trying to obtain and

extract protein sequence motifs which are universally conserved and across protein family boundaries.

Discuss the hidden relation between protein Primary sequences and their Tertiary structure

The Main purpose of this paper In order to obtain the DNA/protein

sequence motifs information, fixing the length of sequence segments is usually necessary.

Due to the fixed size, they might deliver a number of similar motifs simply shifted by several bases or including mismatches

mismatches and shifted by several bases problem

In this paper, we deal with “mismatches” problem

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Association Rules

Association Rules

support, s, probability that a transaction contains X Y

confidence, c, conditional probability that a transaction having X also contains Y

Association Rules

Support (A=>B) = 3/5 Confidence (A=>B) = 3/3

Positional Association Rules Example

Positional Association Rules

Positional Association Rules A=>D minimum distance assurance = 60%

1. = 3/4

2. = 1/4

)(5

DA

)(28

DA

Positional Association Rules By applying positional association

rules into our data set, we obtain two type of rules:

Rules with distance =0 , and

Rules with distance not =0

)(0

DA

)(0

DA

Directed graph generated from positional association rules with distance =0

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Structural Clustering Algorithm for Networks (SCAN)

SCAN was originally designed for Network clustering

Structural Clustering Algorithm for Networks (SCAN)

SCAN has two parameters: ε: Similarity threshold μ: Minimum number of members in a

cluster

Structural Clustering Algorithm for Networks (SCAN)

Similarity is calculated by

Γ(E)={E,B} Γ(B)={E,B,A,C,D}

which is the example of σ(E,B)=Γ(E) ∩ Γ(B) / sqrt(num(Γ(E) )*num(Γ(B)))

= 2/ sqrt(2*5) = 0.63

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Dataset

In our previous work, we discovered 343 protein sequence motifs from 2710 protein sequences

So we mapped those sequences back to those protein sequences

Dataset

Therefore, the dataset we work on equals to 2710 transactions and 343 data items

Evaluation of the cluster The quality of the cluster

is evaluated by secondary structural similarity

If the structural homology for a cluster exceeds 70%, the cluster can be considered structurally identical.

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Distance Assurance effects most ε=0.3 seems generating good results

EPS

μ’s effect on the results

Outline

Introduction Methods

Positional Association Rule SCAN

Dataset Results Conclusion

Conclusion In this paper, we combine

positional association rule and SCAN algorithm to alleviate the mismatch problem caused by fixed window size approach.

We show that the positional association rule algorithm can also be used as clustering manner

Future work

Find the optimal parameters

Improve SCAN into directed graph

Thanks!!

Questions??