{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:11:47Z","timestamp":1775077907359,"version":"3.50.1"},"reference-count":104,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C1014338"],"award-info":[{"award-number":["2021R1A2C1014338"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1I1A1A01062260"],"award-info":[{"award-number":["2019R1I1A1A01062260"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A4A4079722"],"award-info":[{"award-number":["2020R1A4A4079722"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.<\/jats:p>","DOI":"10.1093\/bib\/bbab412","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T12:10:25Z","timestamp":1631189425000},"source":"Crossref","is-referenced-by-count":27,"title":["Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0697-9419","authenticated-orcid":false,"given":"Balachandran","family":"Manavalan","sequence":"first","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea"}]},{"given":"Shaherin","family":"Basith","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1299-9478","authenticated-orcid":false,"given":"Gwang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea"}]}],"member":"286","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"2022011921124581400_ref1","first-page":"328","article-title":"COVID-19: the first documented coronavirus pandemic in history","volume":"43","author":"Liu","year":"2020","journal-title":"Biom J"},{"key":"2022011921124581400_ref2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/s41577-020-0311-8","article-title":"The trinity of COVID-19: immunity, inflammation and intervention","volume":"20","author":"Tay","year":"2020","journal-title":"Nat Rev Immunol"},{"key":"2022011921124581400_ref3","article-title":"Mystery virus found in Wuhan resembles bat viruses but not SARS","journal-title":"Chinese scientist says"},{"key":"2022011921124581400_ref4","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1093\/bib\/bbz107","article-title":"Deep learning based prediction of reversible HAT\/HDAC-specific lysine acetylation","volume":"21","author":"Yu","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref5","doi-asserted-by":"crossref","first-page":"408","DOI":"10.14348\/molcells.2021.0026","article-title":"Molecular perspectives of SARS-CoV-2: pathology, immune evasion, and therapeutic interventions","volume":"44","author":"Shah","year":"2021","journal-title":"Mol Cells"},{"key":"2022011921124581400_ref6"},{"key":"2022011921124581400_ref7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1002\/jmv.25593","article-title":"Why are vaccines against many human viral diseases still unavailable; an historic perspective?","volume":"92","author":"Tannock","year":"2020","journal-title":"J Med Virol"},{"key":"2022011921124581400_ref8","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s12929-017-0328-x","article-title":"Evaluation of the use of therapeutic peptides for cancer treatment","volume":"24","author":"Marqus","year":"2017","journal-title":"J Biomed Sci"},{"key":"2022011921124581400_ref9","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1111\/cbdd.12055","article-title":"The future of peptide-based drugs","volume":"81","author":"Craik","year":"2013","journal-title":"Chem Biol Drug Des"},{"key":"2022011921124581400_ref10","doi-asserted-by":"crossref","first-page":"5736","DOI":"10.1074\/jbc.RA119.007360","article-title":"HIV-1 anchor inhibitors and membrane fusion inhibitors target distinct but overlapping steps in virus entry","volume":"294","author":"Eggink","year":"2019","journal-title":"J Biol Chem"},{"key":"2022011921124581400_ref11","doi-asserted-by":"crossref","DOI":"10.1128\/JVI.00831-17","article-title":"Enfuvirtide (T20)-based lipopeptide is a potent HIV-1 cell fusion inhibitor: implications for viral entry and inhibition","volume":"91","author":"Ding","year":"2017","journal-title":"J Virol"},{"key":"2022011921124581400_ref12","doi-asserted-by":"crossref","first-page":"W199","DOI":"10.1093\/nar\/gks450","article-title":"AVPpred: collection and prediction of highly effective antiviral peptides","volume":"40","author":"Thakur","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref13","doi-asserted-by":"crossref","first-page":"e70166","DOI":"10.1371\/journal.pone.0070166","article-title":"Analysis and prediction of highly effective antiviral peptides based on random forests","volume":"8","author":"Chang","year":"2013","journal-title":"PLoS One"},{"key":"2022011921124581400_ref14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2174\/1875036201509010013","article-title":"Using Chou\u2019s pseudo amino acid composition and machine learning method to predict the antiviral peptides","volume":"9","author":"Zare","year":"2015","journal-title":"Open Bioinform J"},{"key":"2022011921124581400_ref15","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.compbiomed.2019.02.011","article-title":"AntiVPP 1.0: a portable tool for prediction of antiviral peptides","volume":"107","author":"Beltran Lissabet","year":"2019","journal-title":"Comput Biol Med"},{"key":"2022011921124581400_ref16","doi-asserted-by":"crossref","first-page":"19260","DOI":"10.1038\/s41598-020-76161-8","article-title":"Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance","volume":"10","author":"Chowdhury","year":"2020","journal-title":"Sci Rep"},{"key":"2022011921124581400_ref17","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1093\/bioinformatics\/btz246","article-title":"PEPred-suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning","volume":"35","author":"Wei","year":"2019","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref18","doi-asserted-by":"crossref","first-page":"42362","DOI":"10.1038\/srep42362","article-title":"Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC","volume":"7","author":"Meher","year":"2017","journal-title":"Sci Rep"},{"key":"2022011921124581400_ref19","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1093\/bib\/bbz043","article-title":"Characterization and identification of antimicrobial peptides with different functional activities","volume":"21","author":"Chung","year":"2019","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref20","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.compbiomed.2019.02.018","article-title":"AMAP: hierarchical multi-label prediction of biologically active and antimicrobial peptides","volume":"107","author":"Gull","year":"2019","journal-title":"Comput Biol Med"},{"key":"2022011921124581400_ref21","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.1093\/bioinformatics\/btw560","article-title":"Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types","volume":"32","author":"Lin","year":"2016","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref22","doi-asserted-by":"crossref","first-page":"3982","DOI":"10.1093\/bioinformatics\/btaa275","article-title":"PPTPP: a novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref23","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab263","article-title":"AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref24","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1093\/bib\/bbaa423","article-title":"Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies","volume":"22","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref25","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab258","article-title":"ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides","author":"Timmons","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref26","doi-asserted-by":"crossref","DOI":"10.3390\/ijms20225743","article-title":"Meta-iAVP: a sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation","volume":"20","author":"Schaduangrat","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2022011921124581400_ref27","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1093\/bib\/bbab042","article-title":"Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2","volume":"22","author":"Cheng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref28","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2102960118","article-title":"COVID-19 induces lower levels of IL-8, IL-10, and MCP-1 than other acute CRS-inducing diseases","volume":"118","author":"Cheng","year":"2021","journal-title":"Proc Natl Acad Sci"},{"key":"2022011921124581400_ref29","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/S0140-6736(20)30628-0","article-title":"COVID-19: consider cytokine storm syndromes and immunosuppression","volume":"395","author":"Mehta","year":"2020","journal-title":"Lancet"},{"key":"2022011921124581400_ref30","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1016\/S2213-2600(21)00139-9","article-title":"Interleukin-6 receptor blockade in patients with COVID-19: placing clinical trials into context","volume":"9","author":"Angriman","year":"2021","journal-title":"Lancet Respir Med"},{"key":"2022011921124581400_ref31","doi-asserted-by":"crossref","first-page":"e253","DOI":"10.1016\/S2665-9913(21)00012-6","article-title":"Interleukin-1 and interleukin-6 inhibition compared with standard management in patients with COVID-19 and hyperinflammation: a cohort study","volume":"3","author":"Cavalli","year":"2021","journal-title":"Lancet Rheumatol"},{"key":"2022011921124581400_ref32","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s00281-017-0629-x","article-title":"Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology","volume":"39","author":"Channappanavar","year":"2017","journal-title":"Semin Immunopathol"},{"key":"2022011921124581400_ref33","doi-asserted-by":"crossref","first-page":"827","DOI":"10.3389\/fimmu.2020.00827","article-title":"Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19)","volume":"11","author":"Diao","year":"2020","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref34","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"2022011921124581400_ref35","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ijid.2020.06.079","article-title":"The characteristics and predictive role of lymphocyte subsets in COVID-19 patients","volume":"99","author":"Zhang","year":"2020","journal-title":"Int J Infect Dis"},{"key":"2022011921124581400_ref36","doi-asserted-by":"crossref","first-page":"104370","DOI":"10.1016\/j.jcv.2020.104370","article-title":"Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19","volume":"127","author":"Liu","year":"2020","journal-title":"J Clin Virol"},{"key":"2022011921124581400_ref37","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1080\/22221751.2020.1770129","article-title":"Profiling serum cytokines in COVID-19 patients reveals IL-6 and IL-10 are disease severity predictors","volume":"9","author":"Han","year":"2020","journal-title":"Emerg Microbes Infect"},{"key":"2022011921124581400_ref38","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cytogfr.2020.05.009","article-title":"IL-6: relevance for immunopathology of SARS-CoV-2","volume":"53","author":"Gubernatorova","year":"2020","journal-title":"Cytokine Growth Factor Rev"},{"key":"2022011921124581400_ref39","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1016\/j.jhep.2016.02.004","article-title":"IL-6 pathway in the liver: from physiopathology to therapy","volume":"64","author":"Schmidt-Arras","year":"2016","journal-title":"J Hepatol"},{"key":"2022011921124581400_ref40","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1093\/intimm\/dxq030","article-title":"IL-6: from its discovery to clinical applications","volume":"22","author":"Kishimoto","year":"2010","journal-title":"Int Immunol"},{"key":"2022011921124581400_ref41","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/S0140-6736(20)30251-8","article-title":"Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding","volume":"395","author":"Lu","year":"2020","journal-title":"Lancet"},{"key":"2022011921124581400_ref42","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1002\/jmv.20556","article-title":"Cytokine regulation in SARS coronavirus infection compared to other respiratory virus infections","volume":"78","author":"Okabayashi","year":"2006","journal-title":"J Med Virol"},{"key":"2022011921124581400_ref43","doi-asserted-by":"crossref","first-page":"581338","DOI":"10.3389\/fimmu.2020.581338","article-title":"Pro- and anti-inflammatory responses in severe COVID-19-induced acute respiratory distress syndrome-an observational pilot study","volume":"11","author":"Notz","year":"2020","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref44","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/1745-6150-8-30","article-title":"Designing of interferon-gamma inducing MHC class-II binders","volume":"8","author":"Dhanda","year":"2013","journal-title":"Biol Direct"},{"key":"2022011921124581400_ref45","doi-asserted-by":"crossref","first-page":"263952","DOI":"10.1155\/2013\/263952","article-title":"Prediction of IL4 inducing peptides","volume":"2013","author":"Dhanda","year":"2013","journal-title":"Clin Dev Immunol"},{"key":"2022011921124581400_ref46","doi-asserted-by":"crossref","first-page":"42851","DOI":"10.1038\/srep42851","article-title":"Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential","volume":"7","author":"Nagpal","year":"2017","journal-title":"Sci Rep"},{"key":"2022011921124581400_ref47","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.3389\/fimmu.2017.01430","article-title":"IL17eScan: A tool for the identification of peptides inducing IL-17 response","volume":"8","author":"Gupta","year":"2017","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref48","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1093\/protein\/gzn006","article-title":"CytoPred: a server for prediction and classification of cytokines","volume":"21","author":"Lata","year":"2008","journal-title":"Protein Eng Des Sel"},{"key":"2022011921124581400_ref49","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1186\/s12967-016-0928-3","article-title":"ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins","volume":"14","author":"Gupta","year":"2016","journal-title":"J Transl Med"},{"key":"2022011921124581400_ref50","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.3389\/fimmu.2018.01783","article-title":"PIP-EL: a new ensemble learning method for improved proinflammatory peptide predictions","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref51","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s12967-016-1103-6","article-title":"Prediction of anti-inflammatory proteins\/peptides: an insilico approach","volume":"15","author":"Gupta","year":"2017","journal-title":"J Transl Med"},{"key":"2022011921124581400_ref52","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1093\/bib\/bbaa259","article-title":"Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19","volume":"22","author":"Dhall","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref53","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab172","article-title":"StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides","author":"Charoenkwan","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref54","doi-asserted-by":"crossref","first-page":"D288","DOI":"10.1093\/nar\/gkaa991","article-title":"DBAASP v3: database of antimicrobial\/cytotoxic activity and structure of peptides as a resource for development of new therapeutics","volume":"49","author":"Pirtskhalava","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref55","doi-asserted-by":"crossref","first-page":"9785","DOI":"10.1021\/acs.jpcb.0c05621","article-title":"Antiviral peptides as promising therapeutics against SARS-CoV-2","volume":"124","author":"Chowdhury","year":"2020","journal-title":"J Phys Chem B"},{"key":"2022011921124581400_ref56","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.addr.2020.11.007","article-title":"Peptide and peptide-based inhibitors of SARS-CoV-2 entry","volume":"167","author":"Schutz","year":"2020","journal-title":"Adv Drug Deliv Rev"},{"key":"2022011921124581400_ref57","doi-asserted-by":"crossref","DOI":"10.3390\/v13050912","article-title":"Compelling evidence for the activity of antiviral peptides against SARS-CoV-2","volume":"13","author":"Tonk","year":"2021","journal-title":"Viruses"},{"key":"2022011921124581400_ref58","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1093\/bib\/bbz088","article-title":"ACPred-fuse: fusing multi-view information improves the prediction of anticancer peptides","volume":"21","author":"Rao","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab083","article-title":"Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides","volume":"22","author":"Xu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref60","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref61","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1093\/bioinformatics\/btq003","article-title":"CD-HIT suite: a web server for clustering and comparing biological sequences","volume":"26","author":"Huang","year":"2010","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref62","doi-asserted-by":"crossref","first-page":"D339","DOI":"10.1093\/nar\/gky1006","article-title":"The immune epitope database (IEDB): 2018 update","volume":"47","author":"Vita","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref63","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/JBHI.2020.2977091","article-title":"DeepAVP: a dual-channel deep neural network for identifying variable-length antiviral peptides","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"2022011921124581400_ref64","doi-asserted-by":"crossref","first-page":"13","DOI":"10.2174\/1875036201509010013","article-title":"Using Chou\u2019s pseudo amino acid composition and machine learning method to predict the antiviral peptides","volume":"9","author":"Zare","year":"2015","journal-title":"Open Bioinform J"},{"key":"2022011921124581400_ref65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep42362","article-title":"Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou\u2019s general PseAAC","volume":"7","author":"Meher","year":"2017","journal-title":"Sci Rep"},{"key":"2022011921124581400_ref66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-76161-8","article-title":"Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance","volume":"10","author":"Chowdhury","year":"2020","journal-title":"Sci Rep"},{"key":"2022011921124581400_ref67","doi-asserted-by":"crossref","first-page":"111051","DOI":"10.1016\/j.biopha.2020.111051","article-title":"ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides","volume":"133","author":"Timmons","year":"2021","journal-title":"Biomed Pharmacother"},{"key":"2022011921124581400_ref68","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.2174\/1381612826666201102105827","article-title":"In silico approaches for the prediction and analysis of antiviral peptides: a review","volume":"27","author":"Charoenkwan","year":"2020","journal-title":"Curr Pharm Des"},{"key":"2022011921124581400_ref69","doi-asserted-by":"crossref","first-page":"D1147","DOI":"10.1093\/nar\/gkt1191","article-title":"AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses","volume":"42","author":"Qureshi","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref70","doi-asserted-by":"crossref","first-page":"D285","DOI":"10.1093\/nar\/gky1030","article-title":"dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data","volume":"47","author":"Jhong","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref71","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1038\/s41597-019-0154-y","article-title":"DRAMP 2.0, an updated data repository of antimicrobial peptides","volume":"6","author":"Kang","year":"2019","journal-title":"Sci Data"},{"key":"2022011921124581400_ref72","doi-asserted-by":"crossref","first-page":"e54908","DOI":"10.1371\/journal.pone.0054908","article-title":"HIPdb: a database of experimentally validated HIV inhibiting peptides","volume":"8","author":"Qureshi","year":"2013","journal-title":"PLoS One"},{"key":"2022011921124581400_ref73","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2174\/1389202921666200219125625","article-title":"Extremely-randomized-tree-based prediction of N(6)-methyladenosine sites in Saccharomyces cerevisiae","volume":"21","author":"Govindaraj","year":"2020","journal-title":"Curr Genomics"},{"key":"2022011921124581400_ref74","first-page":"1","article-title":"Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework","volume":"22","author":"Wei","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref75","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab167","article-title":"NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning","author":"Hasan","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref76","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genomics"},{"key":"2022011921124581400_ref77","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1093\/bfgp\/elaa028","article-title":"Critical evaluation of web-based DNA N6-methyladenine site prediction tools","volume":"20","author":"Hasan","year":"2021","journal-title":"Brief Funct Genomics"},{"key":"2022011921124581400_ref78","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.omtn.2020.09.010","article-title":"Empirical comparison and analysis of web-based DNA N4-methylcytosine site prediction tools","volume":"22","author":"Manavalan","year":"2020","journal-title":"Molecular Therapy-Nucleic Acids"},{"key":"2022011921124581400_ref79","doi-asserted-by":"crossref","first-page":"D1087","DOI":"10.1093\/nar\/gkv1278","article-title":"APD3: the antimicrobial peptide database as a tool for research and education","volume":"44","author":"Wang","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref80","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1002\/bip.22703","article-title":"AVP-IC50 Pred: multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)","volume":"104","author":"Qureshi","year":"2015","journal-title":"Biopolymers"},{"key":"2022011921124581400_ref81","doi-asserted-by":"crossref","first-page":"W502","DOI":"10.1093\/nar\/gkz452","article-title":"IEDB-AR: immune epitope database-analysis resource in 2019","volume":"47","author":"Dhanda","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022011921124581400_ref82","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.3389\/fimmu.2018.01695","article-title":"iBCE-EL: a new ensemble learning framework for improved linear B-cell epitope prediction","volume":"9","author":"Manavalan","year":"2018","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref83","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1016\/j.omtn.2020.07.034","article-title":"im6A-TS-CNN: identifying the N(6)-methyladenine site in multiple tissues by using the convolutional neural network","volume":"21","author":"Liu","year":"2020","journal-title":"Mol Ther Nucleic Acids"},{"key":"2022011921124581400_ref84","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1093\/bioinformatics\/btaa155","article-title":"iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"2022011921124581400_ref85","doi-asserted-by":"crossref","first-page":"2617","DOI":"10.1016\/j.ymthe.2021.04.004","article-title":"mRNALocater: enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy","volume":"29","author":"Tang","year":"2021","journal-title":"Mol Ther"},{"key":"2022011921124581400_ref86","first-page":"1","article-title":"Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method","volume":"22","author":"Lv","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref87","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab047","article-title":"iDHS-deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network","volume":"22","author":"Dao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref88","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab244","article-title":"DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach","author":"Lv","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref89","doi-asserted-by":"crossref","first-page":"4011","DOI":"10.1038\/s41467-021-24313-3","article-title":"Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications","volume":"12","author":"Song","year":"2021","journal-title":"Nat Commun"},{"key":"2022011921124581400_ref90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbaa125","article-title":"DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy","volume":"22","author":"Xie","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref91","first-page":"106","article-title":"Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms","volume":"21","author":"Wei","year":"2018","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref92","doi-asserted-by":"crossref","first-page":"100991","DOI":"10.1016\/j.isci.2020.100991","article-title":"iDNA-MS: an integrated computational tool for detecting DNA modification sites in multiple genomes","volume":"23","author":"Lv","year":"2020","journal-title":"iScience"},{"key":"2022011921124581400_ref93","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1093\/bib\/bbz048","article-title":"Evaluation of different computational methods on 5-methylcytosine sites identification","volume":"21","author":"Lv","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref94","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1093\/bib\/bbz123","article-title":"A comparison and assessment of computational method for identifying recombination hotspots in Saccharomyces cerevisiae","volume":"21","author":"Yang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbaa312","article-title":"Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification","volume":"22","author":"Liang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref96","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1093\/bib\/bbz177","article-title":"Design powerful predictor for mRNA subcellular location prediction in Homo sapiens","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022011921124581400_ref97","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.omtn.2019.08.011","article-title":"SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome","volume":"18","author":"Basith","year":"2019","journal-title":"Mol Ther Nucleic Acids"},{"key":"2022011921124581400_ref98","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1016\/j.omtn.2019.04.019","article-title":"Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation","volume":"16","author":"Manavalan","year":"2019","journal-title":"Mol Ther Nucleic Acids"},{"key":"2022011921124581400_ref99","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1093\/bfgp\/elaa028","article-title":"Critical evaluation of web-based DNA N6-methyladenine site prediction tools","volume":"20","author":"Hasan","year":"2021","journal-title":"Brief Funct Genomics"},{"key":"2022011921124581400_ref100","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1021\/acsptsci.0c00059","article-title":"The potential of antimicrobial peptides as an antiviral therapy against COVID-19","volume":"3","author":"Elnagdy","year":"2020","journal-title":"ACS Pharmacol Transl Sci"},{"key":"2022011921124581400_ref101","doi-asserted-by":"crossref","first-page":"117788","DOI":"10.1016\/j.lfs.2020.117788","article-title":"The role of biomarkers in diagnosis of COVID-19 - a systematic review","volume":"254","author":"Kermali","year":"2020","journal-title":"Life Sci"},{"key":"2022011921124581400_ref102","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1007\/s00134-020-06065-8","article-title":"IL-6 may be a good biomarker for earlier detection of COVID-19 progression","volume":"46","author":"Wang","year":"2020","journal-title":"Intensive Care Med"},{"key":"2022011921124581400_ref103","doi-asserted-by":"crossref","first-page":"263","DOI":"10.3389\/fimmu.2021.613422","article-title":"IL-6 is a biomarker for the development of fatal SARS-CoV-2 pneumonia","volume":"12","author":"Santa Cruz","year":"2021","journal-title":"Front Immunol"},{"key":"2022011921124581400_ref104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12879-021-05945-8","article-title":"Role of interleukin 6 as a predictive factor for a severe course of Covid-19: retrospective data analysis of patients from a long-term care facility during Covid-19 outbreak","volume":"21","author":"Sabaka","year":"2021","journal-title":"BMC Infect Dis"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab412\/42229802\/bbab412.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab412\/42229802\/bbab412.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T21:16:19Z","timestamp":1642626979000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab412\/6378599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,30]]},"references-count":104,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab412","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,9,30]]},"article-number":"bbab412"}}