Random Forest Algorithm for Enhanced Prediction of Drug Target Interactions
Shikha Mehta1, Sparsh Sharma2, Sagar Anand3, Shreshth Sharma4, Aditi Goel5
1Shikha Mehta*, Department of Computer and Science Engineering, Jaypee Institute of Information Technology, Noida, India.
2Sparsh Sharma, Department of Computer and Science Engineering., Jaypee Institute of Information Technology, Noida, India.
3Sagar Anand, Department of Computer and Science Engineering., Jaypee Institute of Information Technology, Noida, India.
4Shreshth Sharma, Department of Computer and Science Engineering., Jaypee Institute of Information Technology, Noida, India.
5Aditi Goel, Department of Computer and Science Engineering, Jaypee Institute of Information Technology, Noida, India.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2008-2012 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1722029420/2020©BEIESP | DOI: 10.35940/ijitee.D1722.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Random Forest, Drug Target Interactions, Chemogenomic, Genetic Algorithm, Ensemble Approach.
Scope of the Article: Algorithm Engineering