DTLM-DBP:Deep Transfer Learning Models for DNA Binding Proteins Identification

作     者：Sara Saber Uswah Khairuddin Rubiyah Yusof Ahmed Madani 

作者机构：Department of Computer EngineeringFaculty of EngineeringArab Academy of Science and TechnologyEgypt Centre for Artificial Intelligence&RoboticsMalaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3563-3576页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 08[工学] 071102[理学-系统分析与集成] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 0805[工学-材料科学与工程（可授工学、理学学位）] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081103[工学-系统工程] 

基　　金：This paper was funded under the 2020–2021 Industry-International Incentive Grant by Universiti Teknologi Malaysia(Grant Number:Q.K130000.3043.02M12)which was granted to U.Khairuddin F.Behrooz and R.Yusof 

主　　题：DNABPs deep transfer learning AlexNet 8 VGG 16 SVM RF 

摘      要：The identification of DNA binding proteins(DNABPs)is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g.,in the study of the biological,biophysical,and biochemical effects of antibiotics,drugs,and steroids on *** paper presents an efficient approach for DNABPs identification based on deep transfer learning,named“DTLM-DBP.Two transfer learning methods are used in the identification *** first is based on the pre-trained deep learning model as a feature’s extractor and *** different pre-trained Convolutional Neural Networks(CNN),AlexNet 8 and VGG 16,are tested and *** second method uses the deep learning model as a feature’s extractor only and two different classifiers for the identification *** classifiers,Support Vector Machine(SVM)and Random Forest(RF),are tested and *** proposed approach is tested using different DNA proteins *** performance of the identification process is evaluated in terms of identification accuracy,sensitivity,specificity and MCC,with four available DNA proteins datasets:PDB1075,PDB186,PDNA-543,and *** results show that the RF classifier,with VGG-Net pre-trained deep transfer learning features,gives the highest ***-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.