Human iPS/ES Cell-based Models for Predictive Neural Toxicity and Teratogenicity

Description: 

This project applies supervised machine learning to RNAseq gene expression data from pluripotent cells exposed to neurotoxins and non-neurotoxins. Supervised learning is used to construct models to predict the neurotoxicity of new compounds from the gene expression patterns they induce in the same cells.

CS Collaborators: 

C David Page

Campus Collaborators: 

James Thomson
William Murphy

Funding: 

National Institutes of Health