Significant path selection improves the prediction of novel drug-target interactions
Duc‐Hau Le, Dai-Phong Nguyen, Anh-Minh Dao · 2016
Identifying the interactions between drugs and targets is a crucial step in the process of discovering new drugs. There has been a number of computational methods proposed for the problem. Among them, machine learning-based methods usually utilizes the similarity between drugs and between targets to build kernel matrices, which are used to predict novel drug-target interactions with classification models. While network-based methods usually formulate the prediction as a ranking problem where candidate targets are according to a drug of interest and/or its known targets. A common disadvantage of the network-based methods is that they mainly look for novel targets which are close to known targets in the network. In this study, we proposed a method, namely SigTarget, to overcome this limitation. More specifically, SigTarget ranks candidate targets based on a probability with which they connect to known targets by choosing significant links between known and candidate targets. This method was adapted from an algorithm calculating relative importance between nodes in a network. Simulation results show that SigTarget was better than some existing methods such as TBSI, DBSI and RWR for a set of drugs collected from KEGG database. In addition, we showed the ability of SigTarget in predicting novel drug targets by showing that highly ranked candidate targets obtained from SigTarget are also verified in another drug database, DrugBank.