Identication of Antimicrobial Peptides Using Chou’s 5 Step Rul

作     者：Sharaf J.Malebary Yaser Daanial Khan 

作者机构：Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer ScienceSchool of Systems and TechnologyUniversity of Management and TechnologyLahorePakistan 

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

年 卷 期：2021年第6期

页      面：2863-2881页

学科分类：081702[工学-化学工艺] 07[理学] 08[工学] 0817[工学-化学工程与技术] 070305[理学-高分子化学与物理] 0703[理学-化学] 

基　　金：funded by the Deanship of Scientic Research(DSR) King Abdulaziz University(https://www.kau.edu.sa/) Jeddah under Grant No.(DF-794-611-1441).The authors therefore gratefully acknowledge DSR technical and nancial support 

主　　题：Antimicrobial peptides multidrug-resistant antiviral antibacterial cytokine classication 

摘      要：With the advancement in cellular biology,the use of antimicrobial peptides(AMPs)against many drug-resistant pathogens has *** have a broad range of activity and can work as antibacterial,antifungal,antiviral,and sometimes even as anticancer *** traditional methods of distinguishing AMPs from non-AMPs are based only on wet-lab *** experiments are both time-consuming and *** the recent development in bioinformatics more and more researchers are contributing their effort to apply computational models to such *** study proposes a prediction algorithm for classifying AMPs and distinguishing between AMPs and *** proposed methodology uses machine learning algorithms to predict such sequences.A dataset was formulated based on 1902 samples of AMPs and 3997 samples of *** learning algorithms are trained on a xed number of succinct coefcients retaining sequence and composition information of primary *** features are extracted using position relative incidence and statistical *** performance is validated via various validation tests including a 10-fold cross-validation *** overall accuracy of 95.43%was achieved.A comparison of results with existing methodologies shows that the proposed methodology outperformed existing methodologies in terms of prediction accuracy.