Prediction drug target interaction networks based on semi supervised learning method
Predicting interactions between drugs and target proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. In this work, we present an Improved Laplacian Regularized Least Square Method (ILRLS) for drug target interaction prediction. We predict unknown drug target interactions from chemical structure information, genomic sequence information simultaneously and drug protein interaction network space. We obtain the better achievement from enzymes, ion channels, nuclear receptors and GPCRs interaction networks. The result indicates that the method could play a complementary role to the existing prediction methods.
CCC 2016