iRNA-PseTNC:identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition

作     者：Shahid AKBAR Maqsood HAYAT Muhammad IQBAL Muhammad TAHIR Shahid AKBAR;Maqsood HAYAT;Muhammad IQBAL;Muhammad TAHIR

作者机构：Department of Computer ScienceAbdul Wali Khan University MardanKP 23200Pakistan 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2020年第14卷第2期

页      面：451-460页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：We thank to the anonymous reviewers for their careful reading of our manuscript and their useful comments and suggestions 

主　　题：methylcytosine sites PseTNC PseTetraNC hybrid features SVM cross validation test 

摘      要：RNA 5-methylcytosine(m^5C)sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA *** enzymatic mechanism and biological functions of m^5C sites in DNA remain the focusing area of researchers for last few ***,the investigators also targeted m^5C sites in RNA due to its cellular functions,positioning and formation ***,several rudimentary roles of the m^5C in RNA have been explored,but a lot of improvements are still under ***,the identification of RNA methylcytosine sites was carried out via experimental methods,which were very hard,erroneous and time consuming owing to partial availability of recognized *** at the significance of m^5C role in RNA,scientists have diverted their attention from structure to sequence-based *** this regards,an intelligent computational model is proposed in order to identify m^5C sites in RNA with high *** RNA sequences formulation methods namely:pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical *** a sequel,the vector spaces are fused to build a hybrid space in order to compensate the weakness of each *** learning hypotheses are examined to select the best operational engine,which can truly identify the pattern of the target *** strength and generalization of the proposed model are measured using two different cross validation *** reported outcomes reveal that the proposed model achieved 3%better accuracy than that of the highest present approach in the literature so far.