title: Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction.
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.
authors.affiliation: Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), and School of Computer Engineering, Nanyang Technological University, Singapore.|Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.affiliation: Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), and School of Computer Engineering, Nanyang Technological University, Singapore.
authors.forename: Chunyan
authors.initials: C
authors.lastname: Miao
authors.affiliation: Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.
authors.forename: Peilin
authors.initials: P
authors.lastname: Zhao
authors.affiliation: Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore.
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D008024:Ligands; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations; D011506:Proteins