title: Drug target predictions based on heterogeneous graph inference.
abstract: A key issue in drug development is to understand the hidden relationships among drugs and targets. Computational methods for novel drug target predictions can greatly reduce time and costs compared with experimental methods. In this paper, we propose a network based computational approach for novel drug and target association predictions. More specifically, a heterogeneous drug-target graph, which incorporates known drug-target interactions as well as drug-drug and target-target similarities, is first constructed. Based on this graph, a novel graph-based inference method is introduced. Compared with two state-of-the-art methods, large-scale cross-validation results indicate that the proposed method can greatly improve novel target predictions.
authors: Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio 44106, USA. wxw134@case.edu
authors: Wenhui
authors: W
authors: Wang
authors: Sen
authors: S
authors: Yang
authors: Jing
authors: J
authors: Li
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D003196:Computer Graphics; D062313:Databases, Pharmaceutical; D030562:Databases, Protein; D055808:Drug Discovery / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D011506:Proteins / Q000187:drug effects / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins