{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T05:47:44Z","timestamp":1791352064109,"version":"4.3.1"},"reference-count":77,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2014,8,14]],"date-time":"2014-08-14T00:00:00Z","timestamp":1407974400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"DOI":"10.1371\/journal.pone.0105018","type":"journal-article","created":{"date-parts":[[2014,8,14]],"date-time":"2014-08-14T18:56:48Z","timestamp":1408042608000},"page":"e105018","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":170,"title":["iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition"],"prefix":"10.1371","volume":"9","author":[{"given":"Yan","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Shu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Yun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nai-Yang","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo-Chen","family":"Chou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2014,8,14]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"16295","DOI":"10.1074\/jbc.M413111200","article-title":"Protein nitration in a mouse model of familial amyotrophic lateral sclerosis: possible multifunctional role in the pathogenesis","volume":"280","author":"F Casoni","year":"2005","journal-title":"J Biol Chem"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1074\/mcp.M900259-MCP200","article-title":"In vitro and in vivo protein-bound tyrosine nitration characterized by diagonal chromatography","volume":"8","author":"B Ghesquiere","year":"2009","journal-title":"Mol Cell Proteomics"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1074\/mcp.M700417-MCP200","article-title":"Targets of tyrosine nitration in diabetic rat retina","volume":"7","author":"X Zhan","year":"2008","journal-title":"Mol Cell Proteomics"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10822-012-9624-4","article-title":"Computational study of the effects of protein tyrosine nitrations on the catalytic activity of human thymidylate synthase","volume":"27","author":"A Jarmula","year":"2013","journal-title":"J Comput Aided Mol Des"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"3222","DOI":"10.1021\/pr900039c","article-title":"Protein tyrosine nitration: selectivity, physicochemical and biological consequences, denitration, and proteomics methods for the identification of tyrosine-nitrated proteins","volume":"8","author":"N Abello","year":"2009","journal-title":"J Proteome Res"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Feeney MB, Schoneich C (2013) Proteomic Approaches to Analyze Protein Tyrosine Nitration. Antioxid Redox Signal.","DOI":"10.1089\/ars.2012.5058"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Dekker F, Abello N, Wisastra R, Bischoff R (2012) Enrichment and detection of tyrosine-nitrated proteins. Curr Protoc Protein Sci Chapter 14: Unit 14 13.","DOI":"10.1002\/0471140864.ps1413s69"},{"key":"ref8","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":"KC Chou","year":"2011","journal-title":"Journal of Theoretical Biology"},{"key":"ref9","first-page":"e69","article-title":"iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition Nucleic Acids Research","volume":"41","author":"W Chen","year":"2013"},{"key":"ref10","first-page":"701317","article-title":"iEzy-Drug: A web server for identifying the interaction between enzymes and drugs in cellular networking","volume":"2013","author":"JL Min","year":"2013","journal-title":"BioMed Research International"},{"key":"ref11","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":"Y Xu","year":"2013","journal-title":"PeerJ"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jtbi.2013.08.013","article-title":"iCDI-PseFpt: Identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints","volume":"337C","author":"X Xiao","year":"2013","journal-title":"Journal of Theoretical Biology"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.3390\/ijms15034915","article-title":"iNR-Drug: Predicting the interaction of drugs with nuclear receptors in cellular networking","volume":"15","author":"YN Fan","year":"2014","journal-title":"Intenational Journal of Molecular Sciences"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1093\/bioinformatics\/btu083","article-title":"iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition","volume":"30","author":"SH Guo","year":"2014","journal-title":"Bioinformatics"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1093\/bioinformatics\/btt709","article-title":"Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection","volume":"30","author":"B Liu","year":"2014","journal-title":"Bioinformatics"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.3390\/ijms15021746","article-title":"iRSpot-TNCPseAAC: Identify recombination spots with trinucleotide composition and pseudo amino acid components","volume":"15","author":"WR Qiu","year":"2014","journal-title":"Int J Mol Sci"},{"key":"ref17","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","volume":"15","author":"Y Xu","year":"2014","journal-title":"International Journal of Molecular Sciences"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"286419","DOI":"10.1155\/2014\/286419","article-title":"iCTX-Type: A sequence-based predictor for identifying the types of conotoxins in targeting ion channels","volume":"2014","author":"H Ding","year":"2014","journal-title":"BioMed Research International"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Qiu WR, Xiao X, Lin WZ (2014) iMethyl-PseAAC: Identification of protein methylation sites via a pseudo amino acid composition approach. BioMed Research International 2014: ID 947416.","DOI":"10.1155\/2014\/947416"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"623149","DOI":"10.1155\/2014\/623149","article-title":"iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition","volume":"2014","author":"W Chen","year":"2014","journal-title":"Biomed Research International"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Chen W, Feng PM, Deng EZ, Lin H (2014) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Analytical Biochemistry 10.1016\/j.ab.2014.1006.1022.","DOI":"10.1016\/j.ab.2014.06.022"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"16938","DOI":"10.1016\/S0021-9258(19)85285-7","article-title":"A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins","volume":"268","author":"KC Chou","year":"1993","journal-title":"Journal of Biological Chemistry"},{"key":"ref23","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":"KC Chou","year":"1996","journal-title":"Analytical Biochemistry"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1002\/pro.5560040712","article-title":"A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase","volume":"4","author":"KC Chou","year":"1995","journal-title":"Protein Science"},{"key":"ref25","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":"KC Chou","year":"2001","journal-title":"Peptides"},{"key":"ref26","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":"KC Chou","year":"2007","journal-title":"Analytical Biochemistry"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1039\/c0mb00279h","article-title":"GPS-YNO2: computational prediction of tyrosine nitration sites in proteins","volume":"7","author":"Z Liu","year":"2011","journal-title":"Mol Biosyst"},{"key":"ref28","first-page":"e55844","article-title":"iSNO-PseAAC: Predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition PLoS ONE","volume":"8","author":"Y Xu","year":"2013"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1002\/bip.360330910","article-title":"A formulation for correlating properties of peptides and its application to predicting human immunodeficiency virus protease-cleavable sites in proteins","volume":"33","author":"JJ Chou","year":"1993","journal-title":"Biopolymers"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jtbi.2004.07.023","article-title":"SLLE for predicting membrane protein types","volume":"232","author":"M Wang","year":"2005","journal-title":"Journal of Theoretical Biology"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.bbrc.2005.06.075","article-title":"Boosting classifier for predicting protein domain structural class","volume":"334","author":"KY Feng","year":"2005","journal-title":"Biochemical & Biophysical Research Communications"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ab.2013.05.024","article-title":"iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition","volume":"442","author":"PM Feng","year":"2013","journal-title":"Analytical Biochemistry"},{"key":"ref33","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":"KK Kandaswamy","year":"2011","journal-title":"Journal of Theoretical Biology"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"1888","DOI":"10.1021\/pr060167c","article-title":"Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers","volume":"5","author":"KC Chou","year":"2006","journal-title":"Journal of Proteome Research"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ab.2009.07.046","article-title":"A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0","volume":"394","author":"HB Shen","year":"2009","journal-title":"Analytical Biochemistry"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"e72234","DOI":"10.1371\/journal.pone.0072234","article-title":"iGPCR-Drug: A web server for predicting interaction between GPCRs and drugs in cellular networking","volume":"8","author":"X Xiao","year":"2013","journal-title":"PLoS ONE"},{"key":"ref37","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":"KC Chou","year":"2013","journal-title":"Molecular Biosystems"},{"key":"ref38","first-page":"246","article-title":"Prediction of protein cellular attributes using pseudo amino acid composition","volume":"43","author":"KC Chou","year":"2001","journal-title":"PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60)"},{"key":"ref39","first-page":"435","article-title":"Theoretical and experimental biology in one","volume":"6","author":"SX Lin","year":"2013","journal-title":"J Biomedical Science and Engineering (JBiSE)"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1109\/TCBB.2011.117","article-title":"Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou\u2019s Pseudo Amino Acid Composition and on Evolutionary Information","volume":"9","author":"L Nanni","year":"2012","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jtbi.2013.08.037","article-title":"Predicting anticancer peptides with Chou\u2019s pseudo amino acid composition and investigating their mutagenicity via Ames test","volume":"341","author":"Z Hajisharifi","year":"2014","journal-title":"Journal of Theoretical Biology"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.jtbi.2012.06.028","article-title":"Predicting plant protein subcellular multi-localization by Chou\u2019s PseAAC formulation based multi-label homolog knowledge transfer learning","volume":"310","author":"S Mei","year":"2012","journal-title":"Journal of Theoretical Biology"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jtbi.2012.10.033","article-title":"Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou\u2019s pseudo amino acid composition","volume":"318","author":"YK Chen","year":"2013","journal-title":"Journal of Theoretical Biology"},{"key":"ref44","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2174\/1875036201307010041","article-title":"A short survey on genetic sequences, Chou\u2019s pseudo amino acid composition and its combination with fuzzy set theory","volume":"7","author":"DN Georgiou","year":"2013","journal-title":"The Open Bioinformatics Journal"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jtbi.2011.04.017","article-title":"Prediction of GABA(A) receptor proteins using the concept of Chou\u2019s pseudo-amino acid composition and support vector machine","volume":"281","author":"H Mohabatkar","year":"2011","journal-title":"Journal of Theoretical Biology"},{"key":"ref46","doi-asserted-by":"crossref","first-page":"180","DOI":"10.2174\/092986613804725307","article-title":"Predicting Antibacterial Peptides by the Concept of Chou\u2019s Pseudo-amino Acid Composition and Machine Learning Methods","volume":"20","author":"M Khosravian","year":"2013","journal-title":"Protein & Peptide Letters"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"133","DOI":"10.2174\/157340613804488341","article-title":"Prediction of Allergenic Proteins by Means of the Concept of Chou\u2019s Pseudo Amino Acid Composition and a Machine Learning Approach","volume":"9","author":"H Mohabatkar","year":"2013","journal-title":"Medicinal Chemistry"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10969-011-9120-4","article-title":"Prediction of metalloproteinase family based on the concept of Chou\u2019s pseudo amino acid composition using a machine learning approach","volume":"12","author":"M Mohammad Beigi","year":"2011","journal-title":"Journal of Structural and Functional Genomics"},{"key":"ref49","doi-asserted-by":"crossref","first-page":"890","DOI":"10.2174\/092986612801619589","article-title":"Identifying GPCRs and their Types with Chou\u2019s Pseudo Amino Acid Composition: An Approach from Multi-scale Energy Representation and Position Specific Scoring Matrix","volume":"19","author":"R Zia Ur","year":"2012","journal-title":"Protein & Peptide Letters"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1039\/c2mb25280e","article-title":"Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou\u2019s PseAAC via discrete wavelet transform","volume":"8","author":"XY Sun","year":"2012","journal-title":"Molecular BioSystems"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.3390\/ijms15033495","article-title":"PseAAC-General: Fast building various modes of general form of Chou\u2019s pseudo-amino acid composition for large-scale protein datasets","volume":"15","author":"P Du","year":"2014","journal-title":"International Journal of Molecular Sciences"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"287019","DOI":"10.1155\/2013\/287019","article-title":"Signal propagation in protein interaction network during colorectal cancer progression","volume":"2013","author":"Y Jiang","year":"2013","journal-title":"Biomed Res Int"},{"key":"ref53","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\u2019s pseudo-amino acid compositions","volume":"425","author":"P Du","year":"2012","journal-title":"Analytical Biochemistry"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1093\/bioinformatics\/btt072","article-title":"propy: a tool to generate various modes of Chou\u2019s PseAAC","volume":"29","author":"DS Cao","year":"2013","journal-title":"Bioinformatics"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1093\/protein\/14.2.75","article-title":"Using subsite coupling to predict signal peptides","volume":"14","author":"KC Chou","year":"2001","journal-title":"Protein Engineering"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"63","DOI":"10.4236\/ns.2009.12011","article-title":"Review: recent advances in developing web-servers for predicting protein attributes","volume":"2","author":"KC Chou","year":"2009","journal-title":"Natural Science"},{"key":"ref57","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1002\/1097-0134(20010101)42:1<136::AID-PROT130>3.0.CO;2-F","article-title":"Prediction of protein signal sequences and their cleavage sites","volume":"42","author":"KC Chou","year":"2001","journal-title":"PROTEINS: Structure, Function, and Genetics"},{"key":"ref58","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":"KC Chou","year":"2012","journal-title":"Molecular Biosystems"},{"key":"ref59","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":"X Xiao","year":"2011","journal-title":"Journal of Theoretical Biology"},{"key":"ref60","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":"ZC Wu","year":"2011","journal-title":"Molecular BioSystems"},{"key":"ref61","first-page":"634","article-title":"iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins Molecular BioSystems","volume":"9","author":"WZ Lin","year":"2013"},{"key":"ref62","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":"KC Chou","year":"2011","journal-title":"PLoS One"},{"key":"ref63","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":"X Xiao","year":"2013","journal-title":"Analytical Biochemistry"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3109\/10409239509083488","article-title":"Review: Prediction of protein structural classes","volume":"30","author":"KC Chou","year":"1995","journal-title":"Critical Reviews in Biochemistry and Molecular Biology"},{"key":"ref65","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/nprot.2007.494","article-title":"Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms","volume":"3","author":"KC Chou","year":"2008","journal-title":"Nature Protocols"},{"key":"ref66","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.bbrc.2005.06.128","article-title":"Using supervised fuzzy clustering to predict protein structural classes","volume":"334","author":"HB Shen","year":"2005","journal-title":"Biochem Biophys Res Commun"},{"key":"ref67","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.jtbi.2011.10.015","article-title":"Multi-kernel transfer learning based on Chou\u2019s PseAAC formulation for protein submitochondria localization","volume":"293","author":"S Mei","year":"2012","journal-title":"Journal of Theoretical Biology"},{"key":"ref68","doi-asserted-by":"crossref","first-page":"e47843","DOI":"10.1371\/journal.pone.0047843","article-title":"iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties","volume":"7","author":"W Chen","year":"2012","journal-title":"PLoS ONE"},{"key":"ref69","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.compbiolchem.2010.09.002","article-title":"A novel feature representation method based on Chou\u2019s pseudo amino acid composition for protein structural class prediction","volume":"34","author":"SS Sahu","year":"2010","journal-title":"Computational Biology and Chemistry"},{"key":"ref70","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.jtbi.2013.06.034","article-title":"Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou\u2019s pseudo amino acid compositions","volume":"335","author":"C Huang","year":"2013","journal-title":"Journal of Theoretical Biology"},{"key":"ref71","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jtbi.2013.11.021","article-title":"Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou\u2019s pseudo amino acid composition","volume":"344","author":"L Kong","year":"2014","journal-title":"J Theor Biol"},{"key":"ref72","doi-asserted-by":"crossref","first-page":"10410","DOI":"10.3390\/ijms150610410","article-title":"Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou\u2019s Pseudo Amino Acid Composition","volume":"15","author":"C Jia","year":"2014","journal-title":"Int J Mol Sci"},{"key":"ref73","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1093\/protein\/12.12.1041","article-title":"Protein secondary structural content prediction","volume":"12","author":"W Liu","year":"1999","journal-title":"Protein Engineering"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1023\/A:1020696810938","article-title":"Using pair-coupled amino acid composition to predict protein secondary structure content","volume":"18","author":"KC Chou","year":"1999","journal-title":"Journal of Protein Chemistry"},{"key":"ref75","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":"KC Chou","year":"2005","journal-title":"Bioinformatics"},{"key":"ref76","doi-asserted-by":"crossref","first-page":"615","DOI":"10.2174\/1389203023380468","article-title":"Review: Prediction of protein signal sequences","volume":"3","author":"KC Chou","year":"2002","journal-title":"Current Protein and Peptide Science"},{"key":"ref77","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":"KC Chou","year":"2007","journal-title":"Biochem Biophys Res Comm"}],"container-title":["PLoS ONE"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/dx.plos.org\/10.1371\/journal.pone.0105018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T14:18:53Z","timestamp":1649945933000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0105018"}},"subtitle":[],"editor":[{"given":"Reiner Albert","family":"Veitia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2014,8,14]]},"references-count":77,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2014,8,14]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0105018","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,8,14]]}}}