Chapter 4
Matrix and Tensor Factorization
Methods for Toxicogenomic Modeling
and Prediction
Suleiman A. Khan, Tero Aittokallio, Andreas Scherer, Roland Grafström
and Pekka Kohonen
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 × 10
8 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.
Keywords Machine learning · Group factor analysis · Tensor factorization ·
Bayesian modeling · Drug sensitivity · Connectivity Map · NCI-60 · Gene
expression · Biomarkers
S. A. Khan (B) · T. Aittokallio · A. Scherer
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
e-mail: khan.suleiman@gmail.com; suleiman.khan@helsinki.fi
A. Scherer
e-mail: andreasscherer@eatris.eu
T. Aittokallio
Department of Mathematics and Statistics, University of Turku, Turku, Finland
R. Grafström · P. Kohonen
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
Predictomics AB, Stockholm, Sweden
Misvik Biology Oy, Turku, Finland
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_4
57
Matrix and Tensor Factorization
Methods for Toxicogenomic Modeling
and Prediction
Suleiman A. Khan, Tero Aittokallio, Andreas Scherer, Roland Grafström
and Pekka Kohonen
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 × 10
8 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.
Keywords Machine learning · Group factor analysis · Tensor factorization ·
Bayesian modeling · Drug sensitivity · Connectivity Map · NCI-60 · Gene
expression · Biomarkers
S. A. Khan (B) · T. Aittokallio · A. Scherer
Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
e-mail: khan.suleiman@gmail.com; suleiman.khan@helsinki.fi
A. Scherer
e-mail: andreasscherer@eatris.eu
T. Aittokallio
Department of Mathematics and Statistics, University of Turku, Turku, Finland
R. Grafström · P. Kohonen
Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
Predictomics AB, Stockholm, Sweden
Misvik Biology Oy, Turku, Finland
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0_4
57