Cross-organism toxicogenomics with group factor analysis

Abstract

We investigate the problem of detecting toxicogenomic associations that generalize across organisms, that is, statistical dependencies between transcriptional responses of multiple organisms and toxicological outcomes. We apply an interpretable probabilistic model to detect cross-organism toxicogenomic associations and propose an approach for drug toxicity analysis based on the interactive retrieval of drugs with similar toxicogenomic properties. We show that our approach can give relevant information about the properties of a drug even when direct prediction of toxicity is not feasible. Moreover, we show that a search from a cross-organism database can improve accuracy in the analysis.

Keywords:

• Bayesian modeling
• factor modeling
• information retrieval
• multi-view modeling
• toxicogenomics

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Funding

The Academy of Finland (Finnish Centre of Excellence in Computational Inference Research COIN, 251170; Computational Modeling of the Biological Effects of Chemicals, 140057), Finnish Doctoral Programme in Computational Sciences FICS, and Helsinki Doctoral Programme in Computer Science.