A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation.

ABSTRACT: Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model

SUBMITTER: Haider S 

PROVIDER: S-EPMC6176017 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature