Protein acetylation sites with complex-valued polynomial model  

作　　者：Wenzheng BAO Bin YANG 

机构地区：[1]School of Information Engineering,Xuzhou University of Technology,Xuzhou 221018,China [2]School of Information Science and Engineering,Zaozhuang University,Zaozhuang 277160,China

出　　处：《Frontiers of Computer Science》2024年第3期217-226,共10页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.61902337);Xuzhou Science and Technology Plan Project(KC21047);Jiangsu Provincial Natural Science Foundation(No.SBK2019040953);Natural Science Fund for Colleges and Universities in Jiangsu Province(No.19KJB520016);Young Talents of Science and Technology in Jiangsu,the Key Research Program of the Science Foundation of Shandong Province(ZR2020KE001);the talent project of“Qingtan Scholar”of Zaozhuang University,the PhD research startup foundation of Zaozhuang University(No.2014BS13);Zaozhuang University Foundation(No.2015YY02).

摘　　要：Protein acetylation refers to a process of adding acetyl groups(CH3CO-)to lysine residues on protein chains.As one of the most commonly used protein post-translational modifications,lysine acetylation plays an important role in different organisms.In our study,we developed a humanspecific method which uses a cascade classifier of complexvalued polynomial model(CVPM),combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative samples.Complexvalued gene expression programming and differential evolution are utilized to search the optimal CVPM model.We also made a systematic and comprehensive analysis of the acetylation data and the prediction results.The performances of our proposed method are 79.15%in Sp,78.17%in Sn,78.66%in ACC 78.76%in F1,and 0.5733 in MCC,which performs better than other state-of-the-art methods.

关 键 词：protein acetylation complex-valued polynomial model machine learning 

分 类 号：R9[医药卫生—药学] TP18[自动化与计算机技术—控制理论与控制工程]