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.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

“…Our method can be thought of as a multi-directional extension of some recently described matrix factorization techniques for making recommendations2627. Specifically, let m and n represent the number of proteins and chemicals, respectively, and let R  = ( r i , j ) be a m  ×  n matrix of protein-chemical interactions…”

Drug-target interaction prediction: A Bayesian ranking approach

“…Liu et al [31] and in addition to the performance improvements, it also increases computational efficiency.…”

“…Machine learning methods in general leverage features based on the structure of drugs and targets (e.g., [9,[19][20][21]), drugs' side-effects [22], and the knowledge of already confirmed DTIs [23][24][25][26][27][28][29][30][31][32]. In particular, in case of Bipartite Local Models (BLM) [23] and its extensions (e.g.…”

“…Zheng et al [30] proposed the multiple similarities one-class matrix factorization (MSCMF) model with additive regularization based on multiple drug and target similarity matrices. Liu et al [31] proposed neighborhood regularized logistic matrix factorization (NRLMF), with similar regularization terms as in MSCMF, however the similarity matrices were reduced via the nearest neighbor approach.…”

Survey on Probabilistic Models of Low-Rank Matrix Factorizations

“…In collaborative filtering, there are several other Poisson models in which the observations are usually modeled with a Poisson distribution, and these models mainly include [97][98][99][100][101][102][103][104][105]. As a matter of fact, the Poisson factorization roots in the nonnegative matrix factorization and takes advantage of the sparse essence of user behavior data and scales [103].For some probabilistic models with respect to collaborative filtering, the Poisson distribution is changed into other probability distributions and this change deals with logistic function [106][107][108], Heaviside step function [107,109], Gaussian cumulative density function [110] and so on. In addition, side information on the a low-dimensional latent presentations is integrated into probabilistic low-rank matrix factorization models [111][112][113], and the case that the data is missing not at random is taken into consideration [109,114,115].…”