PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of amiasummtspLink to Publisher's site
 
AMIA Jt Summits Transl Sci Proc. 2012; 2012: 95–104.
Published online 2012 March 19.
PMCID: PMC3392067

An Automated Bayesian Framework for Integrative Gene Expression Analysis and Predictive Medicine

Neena Parikh,1,* Amin Zollanvari, PhD,2,3,* and Gil Alterovitz, PhD1,2,3

Abstract

Motivation

This work constructs a closed loop Bayesian Network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases.

Results:

An automated pipeline was successfully constructed. Integrative models were made based on gene expression data obtained from GEO experiments relating to four different diseases using Bayesian statistical methods. Many of these models demonstrated a high level of accuracy and predictive ability. The approach described in this paper can be applied to any complex disorder and can include any number and type of genome-scale studies.

Keywords: Integrative Genomics, Bayesian Network, Multi-network Model, Gene Expression Omnibus

Articles from AMIA Summits on Translational Science Proceedings are provided here courtesy of American Medical Informatics Association