Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.
Journal:
Computational biology and chemistry
Published Date:
Feb 1, 2019
Abstract
BACKGROUND: Identification 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.