Gene Network Sciences, Inc.

Thomas A Neyarapally

Zachary W Pitluk

Iya G Khalil

Colin C Hill

Bruce W Church

Paul D McDonagh

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Growing community of inventors

Boston, MA, United States of America

Thomas A Neyarapally