DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques

Abstract

Drug response classification constitutes a major challenge in personalized medicine. The suitable drug selection for cancer patients is substantial and the drug response prediction is generally based on the target information, genomic cohort and chemical structure. Hence, for classification process, feature selection approaches are highly essential, which is driven by prior knowledge of gene expression signatures, drug targets, and target pathways. Further, the classification is performed to assess the accurate drug response prediction. To the best of our knowledge, this is the first work to assess different optimization techniques for feature selection and performing drug response classification using different classifiers based on effective optimization algorithm using Cancer Cell Line Encyclopaedia- CCLE and Genomics of Drug Sensitivity in Cancer- GDSC dataset. For performing feature selection, Firefly, Whale and Grey wolf optimization algorithms are examined. Further, to perform classification, Adaboost, gradient boost and random forest classifiers are utilized. In addition to that, a newly developed classification approach, namely Discriminative Weight Updated Tuned Deep Multi-Layer Perceptron (DWUT-MLP) is used and compared with the other classifiers. The optimization algorithms with newly developed DWUT-MLP and other existing classification techniques are evaluated and results shows that the effective feature selection algorithm with suitable classification algorithm improves anticancer drug response prediction's accuracy. Thus, this research is substantial in general for choosing an appropriate feature selection approach, has the probability of improving the accuracy from the interpretable proposed classifier model for an indicative anticancer drug response prediction. From the comparative analysis, it shows that the proposed model performs nearly 10–15% better than existing frameworks in classifying the anticancer drug response.