Title: CIAE: class imbalance aware ensemble framework to predict drug side effects

Authors: Kanica Sachdev; Manoj Kumar Gupta

Addresses: School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, India ' School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, India

Abstract: The binding of the drug compounds to certain biological off target proteins causes undesirable side effects or drug toxicology. The determination of drug toxicology at the early steps of drug development would help to economise on money as well as time. The paper proposes a novel framework, class imbalance aware ensemble (CIAE), for the identification of drug side effects using ensemble learning. It employs the related side effect information of the drugs to predict novel side effects. An eminent cause of the low performance of the machine learning based methods is the presence of class imbalance in the data. The proposed framework efficiently addressees this issue to improve the predictor performance. A comprehensive comparison of the method with the state-of-the-art classifiers shows that the proposed framework yields better results for drug side effect determination.

Keywords: biological targets; class imbalance; drugs; drug side effects; drug toxicology; ensemble classifier; machine learning.

DOI: 10.1504/IJMEI.2023.133089

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.5, pp.458 - 469

Received: 01 Apr 2021
Accepted: 26 Jun 2021
Published online: 01 Sep 2023 *

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