An automated bayesian framework for integrative gene expression analysis and predictive medicine.
BACKGROUND
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.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA;
Neena
N
Parikh
Amin
A
Zollanvari
Gil
G
Alterovitz