Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization.
BACKGROUND
Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug-target interactions and formulating successful computational methods for predicting interactions remains challenging.


RESULTS
In this study, we consider four different drug-target interaction networks from humans involving enzymes, ion channels, G-protein-coupled receptors and nuclear receptors. We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug-target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. The novelty of our approach comes from the joint Bayesian formulation of projecting drug compounds and target proteins into a unified subspace using the similarities and estimating the interaction network in that subspace. We propose using a variational approximation in order to obtain an efficient inference scheme and give its detailed derivations. Finally, we demonstrate the performance of our proposed method in three different scenarios: (i) exploratory data analysis using low-dimensional projections, (ii) predicting interactions for the out-of-sample drug compounds and (iii) predicting unknown interactions of the given network.


BACKGROUND
Software and Supplementary Material are available at http://users.ics.aalto.fi/gonen/kbmf2k.


BACKGROUND
mehmet.gonen@aalto.fi


BACKGROUND
Supplementary data are available at Bioinformatics online.
Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Espoo, Finland. mehmet.gonen@aalto.fi
Mehmet
M
Gönen
D000465:Algorithms; D001499:Bayes Theorem; D055808:Drug Discovery; D004798:Enzymes / Q000187:drug effects; D023281:Genomics; D006801:Humans; D007473:Ion Channels / Q000187:drug effects; D004364:Pharmaceutical Preparations / Q000737:chemistry; D011506:Proteins / Q000737:chemistry / Q000187:drug effects* / Q000235:genetics; D018160:Receptors, Cytoplasmic and Nuclear / Q000187:drug effects; D043562:Receptors, G-Protein-Coupled / Q000187:drug effects; D020539:Sequence Analysis, Protein
D004798:Enzymes; D007473:Ion Channels; D004364:Pharmaceutical Preparations; D011506:Proteins; D018160:Receptors, Cytoplasmic and Nuclear; D043562:Receptors, G-Protein-Coupled