Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods |
| |
Institution: | 1. School of Computer Science and Technology, Tianjin University, Tianjin 300350, China;2. Tianjin University Institute of Computational Biology, Tianjin 300350, China;3. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA;1. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China;2. School of Computer Science, Wuhan University, Wuhan 430072, China;3. School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China;1. Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India;2. Department of Electrical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India;3. School of Artificial Intelligence, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India |
| |
Abstract: | BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions. |
| |
Keywords: | Drug-target interaction Multiple kernel learning Clustering Bi-random walk |
本文献已被 ScienceDirect 等数据库收录! |
|