{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:24:35Z","timestamp":1773343475772,"version":"3.50.1"},"reference-count":89,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T00:00:00Z","timestamp":1559520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["C2017209244"],"award-info":[{"award-number":["C2017209244"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["ZYGX2016J125"],"award-info":[{"award-number":["ZYGX2016J125"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["ZYGX2016J118"],"award-info":[{"award-number":["ZYGX2016J118"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["31771471"],"award-info":[{"award-number":["31771471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Scientific Foundation of China","doi-asserted-by":"crossref","award":["61772119"],"award-info":[{"award-number":["61772119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,21]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.<\/jats:p>","DOI":"10.1093\/bib\/bbz048","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T15:20:19Z","timestamp":1554218419000},"page":"982-995","source":"Crossref","is-referenced-by-count":111,"title":["Evaluation of different computational methods on 5-methylcytosine sites identification"],"prefix":"10.1093","volume":"21","author":[{"given":"Hao","family":"Lv","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Zi-Mei","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Shi-Hao","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Jiu-Xin","family":"Tan","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"},{"name":"Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China"}]},{"given":"Hao","family":"Lin","sequence":"first","affiliation":[{"name":"Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"286","published-online":{"date-parts":[[2019,6,3]]},"reference":[{"key":"2020051819282693600_ref1","doi-asserted-by":"crossref","first-page":"D259","DOI":"10.1093\/nar\/gkv1036","article-title":"RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data","volume":"44","author":"Sun","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2020051819282693600_ref2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2018.04.082","article-title":"Integration of deep feature representations and handcrafted features to improve the prediction of N 6-methyladenosine sites","volume":"324","author":"Wei","year":"2019","journal-title":"Neurocomputing"},{"key":"2020051819282693600_ref3","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1261\/rna.069112.118","article-title":"Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA","volume":"25","author":"Zou","year":"2019","journal-title":"RNA"},{"key":"2020051819282693600_ref4","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nrg.2016.47","article-title":"RNA modifications: what have we learned and where are we headed?","volume":"17","author":"Frye","year":"2016","journal-title":"Nat Rev Genet"},{"key":"2020051819282693600_ref5","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/nmeth.4110","article-title":"Epitranscriptome sequencing technologies: decoding RNA modifications","volume":"14","author":"Li","year":"2016","journal-title":"Nat Methods"},{"key":"2020051819282693600_ref6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/nrm.2016.132","article-title":"Post-transcriptional gene regulation by mRNA modifications","volume":"18","author":"Zhao","year":"2017","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2020051819282693600_ref7","doi-asserted-by":"crossref","first-page":"10249","DOI":"10.1021\/bi00089a047","article-title":"5-Methylcytidine is required for cooperative binding of Mg2+ and a conformational transition at the anticodon stem-loop of yeast phenylalanine tRNA","volume":"32","author":"Chen","year":"1993","journal-title":"Biochemistry"},{"key":"2020051819282693600_ref8","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1128\/MCB.17.1.378","article-title":"Nop2p is required for pre-rRNA processing and 60S ribosome subunit synthesis in yeast","volume":"17","author":"Hong","year":"1997","journal-title":"Mol Cell Biol"},{"key":"2020051819282693600_ref9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.molcel.2005.10.036","article-title":"Rapid tRNA decay can result from lack of nonessential modifications","volume":"21","author":"Alexandrov","year":"2006","journal-title":"Mol Cell"},{"key":"2020051819282693600_ref10","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1101\/gad.586710","article-title":"RNA methylation by Dnmt2 protects transfer RNAs against stress-induced cleavage","volume":"24","author":"Schaefer","year":"2010","journal-title":"Genes Dev"},{"key":"2020051819282693600_ref11","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1038\/ncomms1692","article-title":"The tRNA methyltransferase NSun2 stabilizes p16INK(4) mRNA by methylating the 3\u2032-untranslated region of p16","volume":"3","author":"Zhang","year":"2012","journal-title":"Nat Commun"},{"key":"2020051819282693600_ref12","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1128\/MCB.01523-12","article-title":"The mouse cytosine-5 RNA methyltransferase NSun2 is a component of the chromatoid body and required for testis differentiation","volume":"33","author":"Hussain","year":"2013","journal-title":"Mol Cell Biol"},{"key":"2020051819282693600_ref13","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1261\/rna.043398.113","article-title":"A cluster of methylations in the domain IV of 25S rRNA is required for ribosome stability","volume":"20","author":"Gigova","year":"2014","journal-title":"RNA"},{"key":"2020051819282693600_ref14","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1003602","article-title":"Transcriptome-wide mapping of 5-methylcytidine RNA modifications in bacteria, archaea, and yeast reveals m5C within archaeal mRNAs","volume":"9","author":"Edelheit","year":"2013","journal-title":"PLoS Genet"},{"key":"2020051819282693600_ref15","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1073\/pnas.89.5.1827","article-title":"A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands","volume":"89","author":"Frommer","year":"1992","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2020051819282693600_ref16","doi-asserted-by":"crossref","first-page":"3057","DOI":"10.1007\/s00018-017-2521-1","article-title":"Ultrastructural localization of 5-methylcytosine on DNA and RNA","volume":"74","author":"Masiello","year":"2017","journal-title":"Cell Mol Life Sci"},{"key":"2020051819282693600_ref17","doi-asserted-by":"crossref","DOI":"10.1101\/572990","article-title":"Cascaded-CNN: deep learning to predict protein backbone structure from high-resolution cryo-EM density maps","author":"Moritz","year":"2019"},{"key":"2020051819282693600_ref18","doi-asserted-by":"crossref","DOI":"10.1002\/prot.25697","article-title":"Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13","author":"Hou","year":"2019"},{"key":"2020051819282693600_ref19","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1039\/C6MB00471G","article-title":"Identifying RNA 5-methylcytosine sites via pseudo nucleotide compositions","volume":"12","author":"Feng","year":"2016","journal-title":"Mol Biosyst"},{"key":"2020051819282693600_ref20","doi-asserted-by":"crossref","first-page":"41178","DOI":"10.18632\/oncotarget.17104","article-title":"iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition","volume":"8","author":"Qiu","year":"2017","journal-title":"Oncotarget"},{"key":"2020051819282693600_ref21","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ab.2018.03.027","article-title":"Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble","volume":"550","author":"Zhang","year":"2018","journal-title":"Anal Biochem"},{"key":"2020051819282693600_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jtbi.2018.04.037","article-title":"Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC","volume":"452","author":"Sabooh","year":"2018","journal-title":"J Theor Biol"},{"key":"2020051819282693600_ref23","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-35502-4","article-title":"RNAm5Cfinder: a web-server for predicting RNA 5-methylcytosine (m5C) sites based on random forest","volume":"8","author":"Li","year":"2018","journal-title":"Sci Rep"},{"key":"2020051819282693600_ref24","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3389\/fpls.2018.00519","article-title":"Transcriptome-wide annotation of m(5)C RNA modifications using machine learning","volume":"9","author":"Song","year":"2018","journal-title":"Front Plant Sci"},{"key":"2020051819282693600_ref25","article-title":"Sequence clustering in bioinformatics: an empirical study","author":"Zou","year":"2019","journal-title":"Brief Bioinform"},{"key":"2020051819282693600_ref26","first-page":"3150","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","author":"Fu","year":"2012"},{"key":"2020051819282693600_ref27","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.molp.2017.09.013","article-title":"5-Methylcytosine RNA methylation in Arabidopsis Thaliana","volume":"10","author":"Cui","year":"2017","journal-title":"Mol Plant"},{"key":"2020051819282693600_ref28","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ab.2014.12.009","article-title":"iDNA-methyl: identifying DNA methylation sites via pseudo trinucleotide composition","volume":"474","author":"Liu","year":"2015","journal-title":"Anal Biochem"},{"key":"2020051819282693600_ref29","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1080\/07391102.2014.998710","article-title":"iDrug-target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach","volume":"33","author":"Xiao","year":"2015","journal-title":"J Biomol Struct Dyn"},{"key":"2020051819282693600_ref30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ab.2007.07.006","article-title":"Recent progress in protein subcellular location prediction","volume":"370","author":"Chou","year":"2007","journal-title":"Anal Biochem"},{"key":"2020051819282693600_ref31","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":"2020051819282693600_ref32","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":"2020051819282693600_ref33","article-title":"Identifying sigma70 promoters with novel pseudo nucleotide composition","author":"Lin","year":"2017","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2020051819282693600_ref34","article-title":"Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique","author":"Dao","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref35","article-title":"iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators","author":"Feng","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref36","article-title":"Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species","author":"Wei","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jtbi.2018.07.035","article-title":"Convex hull analysis of evolutionary and phylogenetic relationships between biological groups","volume":"456","author":"Tian","year":"2018","journal-title":"J Theor Biol"},{"key":"2020051819282693600_ref38","doi-asserted-by":"crossref","DOI":"10.1371\/annotation\/22351496-73dc-4205-9d9a-95a821ae74ca","article-title":"A novel method of characterizing genetic sequences: genome space with biological distance and applications","volume":"6","author":"Deng","year":"2011","journal-title":"PLoS One"},{"key":"2020051819282693600_ref39","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 Pept Lett"},{"key":"2020051819282693600_ref40","doi-asserted-by":"crossref","first-page":"433","DOI":"10.3389\/fgene.2018.00433","article-title":"Classifying included and excluded exons in exon skipping event using histone modifications","volume":"9","author":"Chen","year":"2018","journal-title":"Front Genet"},{"key":"2020051819282693600_ref41","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2020051819282693600_ref42","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.tiv.2017.02.016","article-title":"Novel naive Bayes classification models for predicting the chemical Ames mutagenicity","volume":"41","author":"Zhang","year":"2017","journal-title":"Toxicol In Vitro"},{"key":"2020051819282693600_ref43","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.fct.2016.09.005","article-title":"Novel naive Bayes classification models for predicting the carcinogenicity of chemicals","volume":"97","author":"Zhang","year":"2016","journal-title":"Food Chem Toxicol"},{"key":"2020051819282693600_ref44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.23919\/PICMET.2018.8481823","article-title":"Artificial intelligence on job-hopping forecasting: AI on job-hopping","volume-title":"2018 Portland International Conference on Management of Engineering and Technology (PICMET)","author":"Kosylo","year":"2018"},{"key":"2020051819282693600_ref45","doi-asserted-by":"crossref","DOI":"10.1155\/2013\/567529","article-title":"Identification of antioxidants from sequence information using naive Bayes","volume":"2013","author":"Feng","year":"2013","journal-title":"Comput Math Methods Med"},{"key":"2020051819282693600_ref46","doi-asserted-by":"crossref","DOI":"10.1155\/2013\/530696","article-title":"Naive Bayes classifier with feature selection to identify phage virion proteins","volume":"2013","author":"Feng","year":"2013","journal-title":"Comput Math Methods Med"},{"key":"2020051819282693600_ref47","volume-title":"An Introduction to Bayesian Networks","author":"Jensen","year":"1996"},{"key":"2020051819282693600_ref48","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","article-title":"The regression analysis of binary sequences","author":"Cox","year":"1958","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2020051819282693600_ref49","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3389\/fgene.2018.00515","article-title":"Predicting diabetes mellitus with machine learning techniques","volume":"9","author":"Zou","year":"2018","journal-title":"Front Genet"},{"key":"2020051819282693600_ref50","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/21.97458","article-title":"A survey of decision tree classifier","volume":"21","author":"Safavian","year":"1991","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"2020051819282693600_ref51","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1093\/bioinformatics\/bth261","article-title":"Data mining in bioinformatics using Weka","volume":"20","author":"Frank","year":"2004","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref52","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min Knowl Discov"},{"key":"2020051819282693600_ref53","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz015","article-title":"i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref54","doi-asserted-by":"crossref","DOI":"10.1142\/S1793524517500504","article-title":"A two-step discriminated method to identify thermophilic proteins","author":"Tang","year":"2017"},{"key":"2020051819282693600_ref55","doi-asserted-by":"crossref","first-page":"79","DOI":"10.2174\/157016461302160514000940","article-title":"Protein folds prediction with hierarchical structured SVM","volume":"13","author":"Li","year":"2016","journal-title":"Current Proteomics"},{"key":"2020051819282693600_ref56","doi-asserted-by":"crossref","first-page":"476","DOI":"10.3389\/fmicb.2018.00476","article-title":"PVP-SVM: sequence-based prediction of phage Virion proteins using a support vector machine","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front Microbiol"},{"key":"2020051819282693600_ref57","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ab.2014.04.032","article-title":"Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns","volume":"458","author":"Zuo","year":"2014","journal-title":"Anal Biochem"},{"key":"2020051819282693600_ref58","first-page":"27","article-title":"LIBSVM: a library for support vector machines","author":"Chang","year":"2011"},{"key":"2020051819282693600_ref59","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1089\/cmb.2018.0004","article-title":"iRNA-2OM: a sequence-based predictor for identifying 2\u2032-O-methylation sites in Homo sapiens","volume":"25","author":"Yang","year":"2018","journal-title":"J Comput Biol"},{"key":"2020051819282693600_ref60","doi-asserted-by":"crossref","first-page":"957","DOI":"10.7150\/ijbs.24174","article-title":"HBPred: a tool to identify growth hormone-binding proteins","volume":"14","author":"Tang","year":"2018","journal-title":"Int J Biol Sci"},{"key":"2020051819282693600_ref61","article-title":"iProt-sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites","author":"Song","year":"2018","journal-title":"Brief Bioinform"},{"key":"2020051819282693600_ref62","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1093\/bioinformatics\/btx670","article-title":"PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy","volume":"34","author":"Song","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref63","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1021\/acs.jproteome.8b00148","article-title":"Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy","volume":"17","author":"Manavalan","year":"2018","journal-title":"J Proteome Res"},{"key":"2020051819282693600_ref64","doi-asserted-by":"crossref","first-page":"2029","DOI":"10.1093\/bioinformatics\/bty039","article-title":"O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique","volume":"34","author":"Jia","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref65","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.jtbi.2017.03.031","article-title":"S-SulfPred: a sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique","volume":"422","author":"Jia","year":"2017","journal-title":"J Theor Biol"},{"key":"2020051819282693600_ref66","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1101\/gr.849004","article-title":"WebLogo: a sequence logo generator","volume":"14","author":"Crooks","year":"2004","journal-title":"Genome Res"},{"key":"2020051819282693600_ref67","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1105\/tpc.16.00751","article-title":"Transcriptome-wide mapping of RNA 5-methylcytosine in Arabidopsis mRNAs and noncoding RNAs","volume":"29","author":"David","year":"2017","journal-title":"Plant Cell"},{"key":"2020051819282693600_ref68","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1038\/cr.2017.55","article-title":"5-methylcytosine promotes mRNA export\u2014NSUN2 as the methyltransferase and ALYREF as an m(5)C reader","volume":"27","author":"Yang","year":"2017","journal-title":"Cell Res"},{"key":"2020051819282693600_ref69","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3109\/10409239509083488","article-title":"Prediction of protein structural classes","volume":"30","author":"Chou","year":"1995","journal-title":"Crit Rev Biochem Mol Biol"},{"key":"2020051819282693600_ref70","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btz040","article-title":"Protein fold recognition based on multi-view Modeling","author":"Yan","year":"2019","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref71","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1093\/bib\/bbx126","article-title":"A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction","volume":"20","author":"Liu","year":"2019","journal-title":"Brief Bioinform"},{"key":"2020051819282693600_ref72","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"Kaminski","year":"2018","journal-title":"Cent Eur J Oper Res"},{"key":"2020051819282693600_ref73","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1016\/S0895-4356(96)00236-3","article-title":"A simulation study of the number of events per variable in logistic regression analysis","volume":"49","author":"Peduzzi","year":"1996","journal-title":"J Clin Epidemiol"},{"key":"2020051819282693600_ref74","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1038\/nature01262","article-title":"Initial sequencing and comparative analysis of the mouse genome","volume":"420","author":"Mouse Genome Sequencing","year":"2002","journal-title":"Nature"},{"issue":"546","key":"2020051819282693600_ref75","first-page":"563","article-title":"Life with 6000 genes","volume":"274","author":"Goffeau","year":"1996","journal-title":"Science"},{"key":"2020051819282693600_ref76","doi-asserted-by":"crossref","DOI":"10.3390\/molecules22101732","article-title":"ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network","volume":"22","author":"Cao","year":"2017","journal-title":"Molecules"},{"key":"2020051819282693600_ref77","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1093\/bioinformatics\/btw694","article-title":"QAcon: single model quality assessment using protein structural and contact information with machine learning techniques","volume":"33","author":"Cao","year":"2017","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref78","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.18632\/oncotarget.23099","article-title":"DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest","volume":"9","author":"Manavalan","year":"2018","journal-title":"Oncotarget"},{"key":"2020051819282693600_ref79","doi-asserted-by":"crossref","first-page":"W406","DOI":"10.1093\/nar\/gkw336","article-title":"3Drefine: an interactive web server for efficient protein structure refinement","volume":"44","author":"Bhattacharya","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2020051819282693600_ref80","article-title":"Protein single-model quality assessment by feature-based probability density functions","volume":"6","author":"Cao","year":"2016","journal-title":"Sci Rep"},{"key":"2020051819282693600_ref81","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1093\/bioinformatics\/btw564","article-title":"PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition","volume":"33","author":"Zuo","year":"2017","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref82","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1093\/bioinformatics\/bty002","article-title":"DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function","volume":"34","author":"Cheng","year":"2018","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref83","doi-asserted-by":"crossref","first-page":"D115","DOI":"10.1093\/nar\/gkw1052","article-title":"RAID v2.0: an updated resource of RNA-associated interactions across organisms","volume":"45","author":"Yi","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2020051819282693600_ref84","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1093\/bioinformatics\/btw630","article-title":"Pro54DB: a database for experimentally verified sigma-54 promoters","volume":"33","author":"Liang","year":"2017","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref85","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.1093\/bioinformatics\/btx223","article-title":"DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses","volume":"33","author":"Yang","year":"2017","journal-title":"Bioinformatics"},{"key":"2020051819282693600_ref86","doi-asserted-by":"crossref","first-page":"D271","DOI":"10.1093\/nar\/gkr922","article-title":"MimoDB 2.0: a mimotope database and beyond","volume":"40","author":"Huang","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2020051819282693600_ref87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep34820","article-title":"OAHG: an integrated resource for annotating human genes with multi-level ontologies","volume":"6","author":"Cheng","year":"2016","journal-title":"Sci Rep"},{"key":"2020051819282693600_ref88","doi-asserted-by":"crossref","first-page":"D140","DOI":"10.1093\/nar\/gky1051","article-title":"LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse","volume":"47","author":"Cheng","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2020051819282693600_ref89","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1093\/bib\/bbx103","article-title":"MetSigDis: a manually curated resource for the metabolic signatures of diseases","volume":"20","author":"Cheng","year":"2019","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/3\/982\/33227289\/bbz048.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/21\/3\/982\/33227289\/bbz048.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T10:48:28Z","timestamp":1721126908000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/21\/3\/982\/5510088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,3]]},"references-count":89,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,6,3]]},"published-print":{"date-parts":[[2020,5,21]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbz048","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,5]]},"published":{"date-parts":[[2019,6,3]]}}}