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.