Collaborative filtering recommender system for predicting drugs for prostate cancer

Abstract

Prostate cancer is a common type of cancer found in men. Identifying drug targets and inhibitors in drug designing is a challenging task. The Recommender systems (RSs) are regarded as a useful tool and are further considered as optimistic method. The use of tool reflects unprecedented growth and development and a tremendous impact on e-commerce. In the research work, for making the prediction in context of cancer activity class (active/inactive) for compounds extracted from ChEMBL, the RS Methods was used. There are two RS approaches that: Collaborative filtering and Content-based Filtering. From these approaches Collaborative Filtering is applied and successfully conducted the investigation and evaluation for making effective prediction over classes for compounds. In the conducted research the interactions among some of the compounds are known. Further this way prediction of interaction profiles could be conducted. The gathered result from classification is considered as relatively good prediction and maintains the quality. Then we applied various regression techniques on data set which are Lasso, EN (Elastic Net), CART (Classification and regression trees), KNN (k-nearest neighbors), SVR (Support vector regression), RFR (Random forest regression), GBR (Gradient boosting regression) and ETR (Extra tree regression). After analyzing the data set with regression techniques, we compare their results and then we get best results from SVR technique and this technique can be used to find compounds to fight against prostate cancer in lesser time with more efficiency.