Matrix and Tensor Factorization Methods for Toxicogenomic Modeling and Prediction

Abstract

Prediction of unexpected, toxic effects of compounds is a key challenge in computational toxicology. Machine learning-based toxicogenomic modeling opens up a systematic means for genomics-driven prediction of toxicity, which has the potential also to unravel novel mechanistic processes that can help to identify underlying links between the molecular makeup of the cells and their toxicological outcomes. This chapter describes the recent big data and machine learning-driven computational methods and tools that enable one to address these key challenges in computational toxicogenomics, with a particular focus on matrix and tensor factorization approaches. Here we describe these approaches by using exemplary application of a data set comprising over 2.5 × 108 data points and 1300 compounds, with the aim of explaining dose-dependent cytotoxic effects by identifying hidden factors/patterns captured in transcriptomics data with links to structural fingerprints of the compounds. Together transcriptomics and structural data are able to predict pathological states in liver and drug toxicity.