Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89929
Title: Neighborhood regularized logistic matrix factorization for drug-target interaction prediction
Authors: Liu, Yong
Wu, Min
Miao, Chunyan
Zhao, Peilin
Li, Xiao-Li
Keywords: DRNTU::Engineering::Computer science and engineering
Neighborhood Regularized Logistic Matrix Factorization
Drug-Target Interaction
Issue Date: 2016
Source: Liu, Y., Wu, M., Miao, C., Zhao, P., & Li, X.-L. (2016). Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLOS Computational Biology, 12(2), e1004760-. doi:10.1371/journal.pcbi.1004760
Series/Report no.: PLOS Computational Biology
Abstract: In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.
URI: https://hdl.handle.net/10356/89929
http://hdl.handle.net/10220/46442
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1004760
Rights: © 2016 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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