MAY 11, 2017 9:00 AM PDT

Keynote Presentation - Using network models to understand common complex disease predisposition and progression

Speaker
  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
    BIOGRAPHY

Abstract

The study of inherited genomic variation through genome wide association studies (GWAS) promised to provide key biologic insight into common diseases of public health significance such as obesity, type II diabetes (T2D), and cancer. While many large studies of these traits have been conducted, the results have been disappointing – identifying loci of small influence which are difficult to replicate across studies. This difficulty, in part, is due to the heterogeneity of underlying trait evolutionary history and complexity of genetics underlying the trait. Analysis using biologic networks embraces this complexity. Using novel methods that examine variation integrated via networks we find that we can identify common pathways across independent data sets that have markedly higher influence. More provocatively, we find that many of these susceptibility pathways are shared across the complex traits obesity, T2D, and liver cancer. This latter observation suggests that it may be possible to both identify individuals at differential risk of developing disease and better understand why an individual’s disease progresses down specific paths.


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MAY 11, 2017 9:00 AM PDT

Keynote Presentation - Using network models to understand common complex disease predisposition and progression


Specialty

Virology

Antibodies

Immunology

Dna Sequencing

Personalized Medicine

Clinical Diagnostics

Immunity

Immunotherapy

Cancer Diagnostics

Flow Cytometry

Bioinformatics

Immuno-Oncology

Gene Expression

Dna

Big Data

Geography

Europe100%

Registration Source

Website Visitors100%

Job Title

Student100%

Organization

Academic Institution100%


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