Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

Abstract

The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.