Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization

author: Mehmet Gönen, Department of Information and Computer Science, Aalto University
published: Oct. 23, 2012,   recorded: September 2012,   views: 141
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Description

Motivation: 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. Lastly, we demonstrate the performance of our proposed method in three different scenarios: (a) exploratory data analysis using low-dimensional projections, (b) predicting interactions for the out-of-sample drug compounds, and (c) predicting unknown interactions of the given network.
Availability: Software and Supplementary Material are available at http://users.ics.tkk.fi/gonen/kbmf2k/

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Reviews and comments:

Comment1 Gauss, January 22, 2015 at 9:50 a.m.:

Dear Mehmet Gönen:
Recently, I have read your paper “Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization”. This is a wonderful work; the method KBMF proposed in this paper was based on strictly mathematical theory. However, I have some confusion about this method KBMF, as in paper section 3.2.2, you have showed the approximate posterior distributions of each factor, but I don’t know how to obtain these results by theoretical derivation. Could you help me?
Expect your reply

Sincerely yours.
Gauss

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