Application of Feature Extraction and Feature Selection Followed by SMOTE to Improve the Prediction of DNA-Binding Proteins

Abstract

DNA-binding protein is applied to compact the DNA and regulate different cellular processes. This protein has been successfully used to handle genetic disorder and critical diseases, such as cancer. Identification of DNA-binding proteins through experimental techniques is always time-consuming and costly. So, reliable automatic computational method is always desirable to detect DNA-binding proteins by using machine learning concept. However, various aspects affect the overall performances of machine learning algorithms, while discriminating DNA-binding proteins and non-DNA-binding proteins. In the current paper, we present a new methodology to cope with all the important factors. Firstly, we extract the explanatory features based on different composition concept. Secondly, a fuzzy rough set-assisted feature selection with harmony search is used to eliminate redundant and/or irrelevant attributes. Thirdly, Synthetic Minority Over-sampling Technique (SMOTE) is applied to produce optimally balanced datasets. Further, we explore the assessment measures of different learning techniques over unreduced, reduced, and optimally balanced reduced datasets. Next, a comparative study is presented to demonstrate the effectiveness of the entire methodology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.