Abstract
Identification of drug-target interaction (DTI) is an important challenge for research and development in the pharmaceutical industry. Biomedicine researchers have stepped from in vitro and in vivo experiments to in-silico methods for fast results. In the recent past, machine learning algorithms have become very popular for DTI predictions. This paper presents an ensemble approach- Random forest algorithm for DTI predictions. The performance of proposed approach is evaluated with respect to Matrix factorization, genetic algorithm, Support vector machines, K-nearest neighbor, Decision Trees and Logistic Regression over 4 benchmark datasets with diverse properties. The algorithm is evaluated over Accuracy and average ranking. Results establish that random forest algorithm is more suitable or DTI predictions as compared to other algorithms.
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