{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T10:54:40Z","timestamp":1775732080839,"version":"3.50.1"},"reference-count":79,"publisher":"Oxford University Press (OUP)","issue":"20","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as \u2018acetylation\u2019, \u2018crotonylation\u2019, \u2018methylation\u2019 and \u2018succinylation\u2019. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none?<\/jats:p><jats:p>Results: To address this problem, a multi-label predictor called iPTM-mLys has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging.<\/jats:p><jats:p>Availability and Implementation: For the convenience of most experimental scientists, a user-friendly web-server for iPTM-mLys has been established at http:\/\/www.jci-bioinfo.cn\/iPTM-mLys, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.<\/jats:p><jats:p>Contact: \u00a0wqiu@gordonlifescience.org, xxiao@gordonlifescience.org, kcchou@gordonlifescience.org<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw380","type":"journal-article","created":{"date-parts":[[2016,6,24]],"date-time":"2016-06-24T05:19:32Z","timestamp":1466745572000},"page":"3116-3123","source":"Crossref","is-referenced-by-count":236,"title":["iPTM-mLys: identifying multiple lysine PTM sites and their different types"],"prefix":"10.1093","volume":"32","author":[{"given":"Wang-Ren","family":"Qiu","sequence":"first","affiliation":[{"name":"1 Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China,"},{"name":"2 Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia MO, USA;"},{"name":"3 Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA,"}]},{"given":"Bi-Qian","family":"Sun","sequence":"additional","affiliation":[{"name":"1 Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China,"}]},{"given":"Xuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"1 Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China,"},{"name":"3 Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA,"}]},{"given":"Zhao-Chun","family":"Xu","sequence":"additional","affiliation":[{"name":"1 Computer Department, Jingdezhen Ceramic Institute, Jingdezhen 333403, China,"}]},{"given":"Kuo-Chen","family":"Chou","sequence":"additional","affiliation":[{"name":"3 Computational Biology, Gordon Life Science Institute, Boston, MA 02478, USA,"},{"name":"4 Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China"},{"name":"5 Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]}],"member":"286","published-online":{"date-parts":[[2016,6,22]]},"reference":[{"key":"2023020113462084500_btw380-B1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.cmpb.2015.07.005","article-title":"Identification of heat shock protein families and J-protein types by incorporating dipeptide composition into Chou's general PseAAC","volume":"122","author":"Ahmad","year":"2015","journal-title":"Comput. Methods Program. Biomed"},{"key":"2023020113462084500_btw380-B2","doi-asserted-by":"crossref","DOI":"10.1007\/s00232-015-9868-8","article-title":"Prediction of protein submitochondrial locations by incorporating dipeptide composition into chou's general pseudo amino acid composition","author":"Ahmad","year":"2016","journal-title":"J. Membr. Biol"},{"key":"2023020113462084500_btw380-B3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learn"},{"key":"2023020113462084500_btw380-B4","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1093\/bioinformatics\/btt072","article-title":"propy: a tool to generate various modes of Chou's PseAAC","volume":"29","author":"Cao","year":"2013","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B5","doi-asserted-by":"crossref","first-page":"W249","DOI":"10.1093\/nar\/gkl233","article-title":"MeMo: a web tool for prediction of protein methylation modifications","volume":"34","author":"Chen","year":"2006","journal-title":"Nucleic Acids Res"},{"key":"2023020113462084500_btw380-B6","doi-asserted-by":"crossref","first-page":"e68","DOI":"10.1093\/nar\/gks1450","article-title":"iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition","volume":"41","author":"Chen","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023020113462084500_btw380-B7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ab.2014.04.001","article-title":"PseKNC: a flexible web-server for generating pseudo K-tuple nucleotide composition","volume":"456","author":"Chen","year":"2014","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B8","doi-asserted-by":"crossref","first-page":"2620","DOI":"10.1039\/C5MB00155B","article-title":"Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences","volume":"11","author":"Chen","year":"2015","journal-title":"Mol BioSyst"},{"key":"2023020113462084500_btw380-B9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/bioinformatics\/btu602","article-title":"PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions","volume":"31","author":"Chen","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B10","doi-asserted-by":"crossref","first-page":"16895","DOI":"10.18632\/oncotarget.7815","article-title":"iACP: a sequence-based tool for identifying anticancer peptides","volume":"7","author":"Chen","year":"2016","journal-title":"Oncotarget"},{"key":"2023020113462084500_btw380-B11","first-page":"e332","article-title":"iRNA-PseU: Identifying RNA pseudouridine sites","volume":"5","author":"Chen","year":"2016","journal-title":"Molecular Therapy - Nucleic Acids"},{"key":"2023020113462084500_btw380-B12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1006\/abio.1996.0001","article-title":"Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins","volume":"233","author":"Chou","year":"1996","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B13","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/prot.1035","article-title":"Prediction of protein cellular attributes using pseudo amino acid composition","volume":"43","author":"Chou","year":"2001","journal-title":"Proteins"},{"key":"2023020113462084500_btw380-B14","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1016\/S0196-9781(01)00540-X","article-title":"Prediction of signal peptides using scaled window","volume":"22","author":"Chou","year":"2001","journal-title":"Peptides"},{"key":"2023020113462084500_btw380-B15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1093\/bioinformatics\/bth466","article-title":"Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes","volume":"21","author":"Chou","year":"2005","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B16","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jtbi.2010.12.024","article-title":"Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review)","volume":"273","author":"Chou","year":"2011","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B17","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1039\/c3mb25555g","article-title":"Some remarks on predicting multi-label attributes in molecular biosystems","volume":"9","author":"Chou","year":"2013","journal-title":"Mol. Biosyst"},{"key":"2023020113462084500_btw380-B18","doi-asserted-by":"crossref","first-page":"218","DOI":"10.2174\/1573406411666141229162834","article-title":"Impacts of bioinformatics to medicinal chemistry","volume":"11","author":"Chou","year":"2015","journal-title":"Med. Chem"},{"key":"2023020113462084500_btw380-B19","first-page":"1","article-title":"An unprecedented revolution in medicinal science","volume":"1","author":"Chou","year":"2015","journal-title":"Proc. MOL2NET (International Conference on Multidisciplinary Sciences)"},{"key":"2023020113462084500_btw380-B20","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1021\/ci049686v","article-title":"Prediction of membrane protein types by incorporating amphipathic effects","volume":"45","author":"Chou","year":"2005","journal-title":"J. Chem. Inf. Model"},{"key":"2023020113462084500_btw380-B21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ab.2007.07.006","article-title":"Review: Recent progresses in protein subcellular location prediction","volume":"370","author":"Chou","year":"2007","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B22","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.bbrc.2007.03.162","article-title":"Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides","volume":"357","author":"Chou","year":"2007","journal-title":"Biochem. Biophys. Res. Comm"},{"key":"2023020113462084500_btw380-B23","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3109\/10409239509083488","article-title":"Review: prediction of protein structural classes","volume":"30","author":"Chou","year":"1995","journal-title":"Crit. Rev. Biochem. Mol. Biol"},{"key":"2023020113462084500_btw380-B24","doi-asserted-by":"crossref","first-page":"e18258","DOI":"10.1371\/journal.pone.0018258","article-title":"iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins","volume":"6","author":"Chou","year":"2011","journal-title":"PLoS One"},{"key":"2023020113462084500_btw380-B25","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1039\/C1MB05420A","article-title":"iLoc-Hum: using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites","volume":"8","author":"Chou","year":"2012","journal-title":"Molecular Biosystems"},{"key":"2023020113462084500_btw380-B26","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.jtbi.2014.09.029","article-title":"Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC","volume":"364","author":"Dehzangi","year":"2015","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B27","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ab.2012.03.015","article-title":"PseAAC-Builder: A cross-platform stand-alone program for generating various special Chou's pseudo-amino acid compositions","volume":"425","author":"Du","year":"2012","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B28","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.3390\/ijms15033495","article-title":"PseAAC-General: Fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets","volume":"15","author":"Du","year":"2014","journal-title":"Int. J. Mol. Sci"},{"key":"2023020113462084500_btw380-B29","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s00232-013-9536-9","article-title":"A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types","volume":"246","author":"Huang","year":"2013","journal-title":"J. Membr. Biol"},{"key":"2023020113462084500_btw380-B30","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jtbi.2015.04.011","article-title":"iPPI-Esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC","volume":"377","author":"Jia","year":"2015","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B31","article-title":"Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition (iPPBS-PseAAC)","author":"Jia","year":"2015","journal-title":"J. Biomol. Struct. Dyn"},{"key":"2023020113462084500_btw380-B32","doi-asserted-by":"crossref","first-page":"34558","DOI":"10.18632\/oncotarget.9148","article-title":"iCar-PseCp: identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC","volume":"7","author":"Jia","year":"2016","journal-title":"Oncotarget"},{"key":"2023020113462084500_btw380-B33","doi-asserted-by":"crossref","first-page":"95.","DOI":"10.3390\/molecules21010095","article-title":"iPPBS-Opt: a sequence-based Ensemble classifier for identifying protein-protein binding sites by optimizing imbalanced training datasets","volume":"21","author":"Jia","year":"2016","journal-title":"Molecules"},{"key":"2023020113462084500_btw380-B34","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ab.2015.12.009","article-title":"iSuc-PseOpt: identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset","volume":"497","author":"Jia","year":"2016","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B35","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.jtbi.2016.01.020","article-title":"pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach","volume":"394","author":"Jia","year":"2016","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B78","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btw387","article-title":"pSumo-CD: Predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC","author":"Jia","year":"2016","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B36","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s00438-015-1108-5","article-title":"iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples","volume":"291","author":"Kabir","year":"2016","journal-title":"Mol. Genet. Genomics"},{"key":"2023020113462084500_btw380-B37","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jtbi.2010.10.037","article-title":"AFP-Pred: a random forest approach for predicting antifreeze proteins from sequence-derived properties","volume":"270","author":"Kandaswamy","year":"2011","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B38","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.jtbi.2014.10.014","article-title":"Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model","volume":"365","author":"Khan","year":"2015","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B39","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.jtbi.2014.10.008","article-title":"Prediction of beta-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine","volume":"365","author":"Kumar","year":"2015","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B40","doi-asserted-by":"crossref","first-page":"12961","DOI":"10.1093\/nar\/gku1019","article-title":"iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition","volume":"42","author":"Lin","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023020113462084500_btw380-B41","doi-asserted-by":"crossref","first-page":"435","DOI":"10.4236\/jbise.2013.64054","article-title":"Theoretical and experimental biology in one \u2014A symposium in honour of Professor Kuo-Chen Chou\u2019s 50th anniversary and Professor Richard Gieg\u00e9\u2019s 40th anniversary of their scientific careers","volume":"6","author":"Lin","year":"2013","journal-title":"J. Biomedical Science and Engineering (JBiSE)"},{"key":"2023020113462084500_btw380-B42","doi-asserted-by":"crossref","first-page":"e24756","DOI":"10.1371\/journal.pone.0024756","article-title":"iDNA-Prot: identification of DNA binding proteins using random forest with grey model","volume":"6","author":"Lin","year":"2011","journal-title":"PLoS ONE"},{"key":"2023020113462084500_btw380-B43","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1039\/c3mb25466f","article-title":"iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins","volume":"9","author":"Lin","year":"2013","journal-title":"Mol. BioSyst"},{"key":"2023020113462084500_btw380-B44","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1093\/bioinformatics\/btu820","article-title":"repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects","volume":"31","author":"Liu","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B45","doi-asserted-by":"crossref","first-page":"W65","DOI":"10.1093\/nar\/gkv458","article-title":"Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences","volume":"43","author":"Liu","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023020113462084500_btw380-B46","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1002\/minf.201400025","article-title":"PseDNA-Pro: DNA-binding protein identification by combining Chou's PseAAC and physicochemical distance transformation","volume":"34","author":"Liu","year":"2015","journal-title":"Mol. Informatics"},{"key":"2023020113462084500_btw380-B47","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1080\/07391102.2015.1014422","article-title":"iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach","volume":"34","author":"Liu","year":"2016","journal-title":"J. Biomol. Struct. Dyn"},{"key":"2023020113462084500_btw380-B48","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1093\/bioinformatics\/btv604","article-title":"iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition","volume":"32","author":"Liu","year":"2016","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B49","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btw186","article-title":"iDHS-EL: Identifying DNase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an en-semble learning framework","author":"Liu","year":"2016","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B50","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ab.2015.12.017","article-title":"pRNAm-PC: Predicting N-methyladenosine sites in RNA sequences via physical-chemical properties","volume":"497","author":"Liu","year":"2016","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B51","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jtbi.2014.04.006","article-title":"Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction","volume":"356","author":"Mondal","year":"2014","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B52","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jtbi.2014.07.003","article-title":"Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition","volume":"360","author":"Nanni","year":"2014","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B53","doi-asserted-by":"crossref","first-page":"50","DOI":"10.2174\/092986612798472875","article-title":"RSARF: prediction of residue solvent accessibility from protein sequence using random forest method","volume":"19","author":"Pugalenthi","year":"2012","journal-title":"Protein Peptide Letters"},{"key":"2023020113462084500_btw380-B54","first-page":"947416","article-title":"iMethyl-PseAAC: Identification of Protein Methylation Sites via a Pseudo Amino Acid Composition Approach","volume":"2014","author":"Qiu","year":"2014","journal-title":"Biomed Res Int (BMRI)"},{"key":"2023020113462084500_btw380-B55","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1080\/07391102.2014.968875","article-title":"iUbiq-Lys: Prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a grey system model","volume":"33","author":"Qiu","year":"2015","journal-title":"Journal of Biomolecular Structure and Dynamics (JBSD)"},{"key":"2023020113462084500_btw380-B56","article-title":"iPhos-PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory","author":"Qiu","year":"2016","journal-title":"Mol. Informatics"},{"key":"2023020113462084500_btw380-B57","article-title":"iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier","author":"Qiu","year":"2016","journal-title":"Oncotarget"},{"key":"2023020113462084500_btw380-B79","doi-asserted-by":"crossref","first-page":"44310","DOI":"10.18632\/oncotarget.10027","article-title":"iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC","volume":"7","author":"Qiu","year":"2016","journal-title":"Oncotarget"},{"key":"2023020113462084500_btw380-B58","doi-asserted-by":"crossref","first-page":"e4920","DOI":"10.1371\/journal.pone.0004920","article-title":"Computational identification of protein methylation sites through bi-profile Bayes feature extraction","volume":"4","author":"Shao","year":"2009","journal-title":"PLoS One"},{"key":"2023020113462084500_btw380-B59","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.bbrc.2007.08.140","article-title":"Signal-3L: a 3-layer approach for predicting signal peptide","volume":"363","author":"Shen","year":"2007","journal-title":"Biochem. Biophys. Res. Comm"},{"key":"2023020113462084500_btw380-B60","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/bip.20640","article-title":"Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells","volume":"85","author":"Shen","year":"2007","journal-title":"Biopolymers"},{"key":"2023020113462084500_btw380-B61","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1039\/C5MB00883B","article-title":"Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique","volume":"12","author":"Tang","year":"2016","journal-title":"Mol. Biosyst"},{"key":"2023020113462084500_btw380-B62","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1093\/bioinformatics\/btv212","article-title":"MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier","volume":"31","author":"Wang","year":"2015","journal-title":"Bioinformatics"},{"key":"2023020113462084500_btw380-B63","doi-asserted-by":"crossref","first-page":"3287","DOI":"10.1039\/c1mb05232b","article-title":"iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites","volume":"7","author":"Wu","year":"2011","journal-title":"Mol. BioSyst"},{"key":"2023020113462084500_btw380-B64","doi-asserted-by":"crossref","first-page":"4","DOI":"10.2174\/092986612798472839","article-title":"iLoc-Gpos: A Multi-Layer Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Gram-Positive Bacterial Proteins","volume":"19","author":"Wu","year":"2012","journal-title":"Protein Peptide Lett"},{"key":"2023020113462084500_btw380-B65","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jtbi.2011.06.005","article-title":"iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites","volume":"284","author":"Xiao","year":"2011","journal-title":"J. Theor. Biol"},{"key":"2023020113462084500_btw380-B66","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.ab.2013.01.019","article-title":"iAMP-2L: A two-level multi-label classifier for identifying antimicrobial peptides and their functional types","volume":"436","author":"Xiao","year":"2013","journal-title":"Anal. Biochem"},{"key":"2023020113462084500_btw380-B67","doi-asserted-by":"crossref","first-page":"34180","DOI":"10.18632\/oncotarget.9057","article-title":"iROS-gPseKNC: predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition","volume":"7","author":"Xiao","year":"2016","journal-title":"Oncotarget"},{"key":"2023020113462084500_btw380-B68","doi-asserted-by":"crossref","first-page":"591","DOI":"10.2174\/1568026615666150819110421","article-title":"Recent progress in predicting posttranslational modification sites in proteins","volume":"16","author":"Xu","year":"2016","journal-title":"Curr Top Med Chem"},{"key":"2023020113462084500_btw380-B69","doi-asserted-by":"crossref","first-page":"e55844.","DOI":"10.1371\/journal.pone.0055844","article-title":"iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition","volume":"8","author":"Xu","year":"2013","journal-title":"PLoS One"},{"key":"2023020113462084500_btw380-B70","doi-asserted-by":"crossref","first-page":"e171","DOI":"10.7717\/peerj.171","article-title":"iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins","volume":"1","author":"Xu","year":"2013","journal-title":"PeerJ"},{"key":"2023020113462084500_btw380-B71","doi-asserted-by":"crossref","first-page":"7594","DOI":"10.3390\/ijms15057594","article-title":"iHyd-PseAAC: Predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition, Int.","volume":"15","author":"Xu","year":"2014","journal-title":"J. Mol. Sci"},{"key":"2023020113462084500_btw380-B72","doi-asserted-by":"crossref","first-page":"e105018","DOI":"10.1371\/journal.pone.0105018","article-title":"iNitro-Tyr: Prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition","volume":"9","author":"Xu","year":"2014","journal-title":"PLoS One"},{"key":"2023020113462084500_btw380-B73","doi-asserted-by":"crossref","first-page":"20072","DOI":"10.3390\/ijms151120072","article-title":"Molecular science for drug development and biomedicine.","volume":"15","author":"Zhong","year":"2014","journal-title":"Int. J. Mol. Sci"},{"key":"2023020113462084500_btw380-B74","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1023\/A:1020713915365","article-title":"An intriguing controversy over protein structural class prediction","volume":"17","author":"Zhou","year":"1998","journal-title":"J. Protein Chem"},{"key":"2023020113462084500_btw380-B75","doi-asserted-by":"crossref","first-page":"216-216.","DOI":"10.2174\/1573406411666141229162618","article-title":"Current progress in structural bioinformatics of protein-biomolecule interactions","volume":"11","author":"Zhou","year":"2015","journal-title":"Med. Chem"},{"key":"2023020113462084500_btw380-B76","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1002\/prot.10251","article-title":"Subcellular location prediction of apoptosis proteins","volume":"50","author":"Zhou","year":"2003","journal-title":"Proteins"},{"key":"2023020113462084500_btw380-B77","doi-asserted-by":"crossref","first-page":"381","DOI":"10.2174\/156802661604151014114030","article-title":"Perspectives in medicinal chemistry","volume":"16","author":"Zhou","year":"2016","journal-title":"Curr. Topics Med. Chem"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/20\/3116\/49021225\/bioinformatics_32_20_3116.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/20\/3116\/49021225\/bioinformatics_32_20_3116.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:32:38Z","timestamp":1692405158000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/20\/3116\/2196525"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,22]]},"references-count":79,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2016,10,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btw380","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,10,15]]},"published":{"date-parts":[[2016,6,22]]}}}