GTB-PPI:Predict Protein-protein Interactions Based on L1-regularized Logistic Regression and Gradient Tree Boosting

作     者：Bin Yu Cheng Chen Hongyan Zhou Bingqiang Liu Qin Ma Bin Yu;Cheng Chen;Hongyan Zhou;Bingqiang Liu;Qin Ma

作者机构：School of Life SciencesUniversity of Science and Technology of ChinaHefei 230027China College of Mathematics and PhysicsQingdao University of Science and TechnologyQingdao 266061China Artificial Intelligence and Biomedical Big Data Research CenterQingdao University of Science and TechnologyQingdao 266061China School of MathematicsShandong UniversityJinan 250100China Department of Biomedical InformaticsCollege of MedicineThe Ohio State UniversityColumbusOH 43210USA 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2020年第18卷第5期

页      面：582-592页

核心收录：

学科分类：0710[理学-生物学] 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] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0703[理学-化学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.61863010) the Key Research and Development Program of Shandong Province of China(Grant No.2019GGX101001) the Natural Science Foundation of Shandong Province of China(Grant No.ZR2018MC007) 

主　　题：Protein-protein interaction Feature fusion L1-regularized logistic regression Gradient tree boosting Machine learning 

摘      要：Protein-protein interactions(PPIs)are of great importance to understand genetic mechanisms,delineate disease pathogenesis,and guide drug *** the increase of PPI data and development of machine learning technologies,prediction and identification of PPIs have become a research hotspot in *** this study,we propose a new prediction pipeline for PPIs based on gradient tree boosting(GTB).First,the initial feature vector is extracted by fusing pseudo amino acid composition(Pse AAC),pseudo position-specific scoring matrix(Pse PSSM),reduced sequence and index-vectors(RSIV),and autocorrelation descriptor(AD).Second,to remove redundancy and noise,we employ L1-regularized logistic regression(L1-RLR)to select an optimal feature ***,GTB-PPI model is ***-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets,*** addition,GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans,Escherichia coli,Homo sapiens,and Mus musculus,the one-core PPI network for CD9,and the crossover PPI network for the Wnt-related signaling *** results show that GTB-PPI can significantly improve accuracy of PPI *** code and datasets of GTB-PPI can be downloaded from https://***/QUST-AIBBDRC/GTB-PPI/.