A generalized method toward drug-target interaction prediction via low-rank matrix projection

Abstract

Drug-target interaction (DTI) prediction plays a very important role in drug development and drug discovery. Biochemical experiments or in vitro methods are very expensive, laborious and time-consuming. Therefore, in silico approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra characteristic information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a method based on low-rank matrix projection to solve the DTI prediction problem. On one hand, when there is no extra characteristic information of drugs or

Network Reconstruction and Controlling Based on Structural Regularity Analysis

“…It is interesting to find that such norm regularization based models have also been applied to drug development [53] and graph mining [54]. Our work is different from the methods, because the current work adopts different matrix norms for network reconstruction and emphasizes the regularity measuring and regulating of networks.…”