Hybrid_DBP: Prediction of DNA-binding Proteins Using Hybrid Features and Convolutional Neural Networks

Overview

Journal Front Pharmacol

Date 2022 Oct 27

PMID 36299898

Authors

Shaoyou Yu

Dejun Peng

Wen Zhu

Bo Liao

Peng Wang

Dongxuan Yang

Fangxiang Wu

Affiliations

Soon will be listed here.

Abstract

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.

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