Using graphs to decipher complexity of health and disease

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EdgeLeap's presentation at the Graph Database Meetup Amsterdam, 5 November 2014 where we showed applications of graphs and Neo4J in life sciences research.

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Thomas Kelder, PhD

thomas@edgeleap.com

Using graphs to decipher

complexity of health and disease

Graph Database Meetup, Amsterdam, 5 November 2014

This presentation remains property of EdgeLeap B.V. and is licensed for reuse under a Creative Commons Attribution 4.0 International License (see http://creativecommons.org/licenses/by/4.0/).

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GRAPHS @ EDGELEAP

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Diameter of the World-wide WebAlbert, Jeong and Barabási, Nature 1999

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PERSONAL HEALTH IS COMPLEX

Organs

Person

Cells

10010110011010111011001010110111010101101010011010000101011011000010101011010010011111000101010110100010001010111011001011001101011101100101011011101010110101001101000010101101100001010101101001001111100010101011010001000101011101100101100110101110110010101101110101011010100110100001010110110000101010110100100111110001010101101000100010101110110010110011010111011001010110111010101101010011010000101011011000010101011010010011111000101010

DATA!

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IMAGINE POPULATION HEALTH!

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ITERATIVE DISCOVERY

INTERVENE

UNDERSTAND

VISUALIZE

MINE

INTEGRATE

MEASURE

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GRAPH OF DISEASES

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GRAPH OF ADIPOSE HEALTH

Kelder, et al., Genes & Nutrition (2014), in press

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NEO4J IN BIOLOGY

CyNeo4J by Georg Summerhttp://cyneo4j.wordpress.com/

Storage & Calculation Analysis & Visualization

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PHARMA CASE:

TARGET PRIORITIZATION

• Target prioritization for drug development

• Narrow down from >1000 potential targets to ~10 best candidates

• Make smart choice, account for complex underlying biology!

• Biological context

• Centrality

• Affected processes

• Druggability

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REACTOME PATHWAY DATABASE

• Known biological pathways

• Relational database

• Complex data model (249 tables)

• Model pathways, events, proteins as graph

• 1,597 signaling pathways

• 7,597 unique proteins

• 6,467 reactions

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EXAMPLE:

SIGNALING PATHWAYS

Signal input

Effect

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PATHWAY IN NEO4J

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PROXIMITY BY SHORTEST PATH

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PROXIMITY FOR ALL PROTEINS

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REAL LIFE EXAMPLE

16

Random

Classical bioinformatics

Prioritization based ongraph models

Benchmark prioritization techniques by recovery of known relevant genes.

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PEAK INTO THE FUTURE

JAN FEB MAR APR MAY JUN

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THANKS!

info@edgeleap.com

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edgeleap

@EdgeLeap

Marijana RadonjicEdgeLeap

Georg SummerUM & TNO