KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Yuran Jia; Shan Huang; Tianjiao Zhang

doi:10.3389/fgene.2021.811158

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Front Genet. 2021 Nov 29:12:811158. doi: 10.3389/fgene.2021.811158. eCollection 2021.

Authors

Yuran Jia¹, Shan Huang², Tianjiao Zhang¹

Affiliations

¹ College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
² Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

Abstract

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

Keywords: DNA-binding protein; feature extraction; multi-feature fusion; position specificity score matrix; random forest.