piEnPred:a bi-layered discriminative model for enhancers and their subtypes via novel cascade multi-level subset feature selection algorithm

作     者：Zaheer Ullah KHAN Dechang PI Shuanglong YAO Asif NAWAZ Farman ALI Shaukat ALI Zaheer Ullah KHAN;Dechang PI;Shuanglong YAO;Asif NAWAZ;Farman ALI;Shaukat ALI

作者机构：School of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Computer ScienceIslamia College University PeshawarPeshawar 25000Pakistan 

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

年 卷 期：2021年第15卷第6期

页      面：175-185页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work was supported by the National Natural Science Foundation of China(Grant No.U1433116) 

主　　题：enhancer enhancer types novel CM-SFS algorithm feature selection SVM 

摘      要：Enhancers are short DNA cis-elements that can be bound by proteins(activators)to increase the possibility that transcription of a particular gene will *** Enhancers perform a significant role in the formation of proteins and regulating the gene transcription *** diseases such as cancer,inflammatory bowel disease,Parkinson’s,addiction,and schizophrenia are due to genetic variation in *** the current study,we havemade an effort by building,amore robust and novel computational a bi-layered *** representative feature vector was constructed over a linear combination of six *** optimum Hybrid feature vector was obtained via the Novel Cascade Multi-Level Subset Feature selection(CMSFS)*** first layer predicts the enhancer,and the secondary layer carries the prediction of their *** baseline model obtained 87.88%of accuracy,95.29%of sensitivity,80.47%of specificity,0.766 of MCC,and 0.9603 of a roc value on ***,the model obtained 68.24%,65.54%,70.95%,0.3654,and 0.7568 as an Accuracy,sensitivity,specificity,MCC,and ROC values on layer-2 *** an independent dataset on layer-1,the piEnPred secured 80.4%accuracy,82.5%of sensitivity,78.4%of specificity,and 0.6099 as MCC,***,the proposed predictor obtained 72.5%of accuracy,70.0%of sensitivity,75%of specificity,and 0.4506 of MCC on layer-2,*** proposed method remarkably performed in contrast to other state-of-the-art *** the convenience of most experimental scientists,a user-friendly and publicly freely accessible web server@/bienhancer dot pythonanywhere dot com has been developed.