{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T19:08:22Z","timestamp":1790190502496,"version":"4.1.0"},"reference-count":62,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Molecular Biology"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.jmb.2024.168853","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T02:38:52Z","timestamp":1730860732000},"page":"168853","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":27,"title":["Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides"],"prefix":"10.1016","volume":"437","author":[{"given":"Phasit","family":"Charoenkwan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pramote","family":"Chumnanpuen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nalini","family":"Schaduangrat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jmb.2024.168853_b0005","first-page":"1","article-title":"A great ape perspective on the origins and evolution of human viruses","volume":"vol. 110","author":"Calvignac-Spencer","year":"2021"},{"key":"10.1016\/j.jmb.2024.168853_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.meegid.2020.104260","article-title":"Genetic diversity and evolution of SARS-CoV-2","volume":"81","author":"Phan","year":"2020","journal-title":"Infect. Genet. Evol."},{"issue":"3","key":"10.1016\/j.jmb.2024.168853_b0015","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1128\/CMR.00102-15","article-title":"Approved antiviral drugs over the past 50 years","volume":"29","author":"De Clercq","year":"2016","journal-title":"Clin. Microbiol. Rev."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0020","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1038\/s41421-020-0169-8","article-title":"The anti-influenza virus drug, arbidol is an efficient inhibitor of SARS-CoV-2 in vitro","volume":"6","author":"Wang","year":"2020","journal-title":"Cell Discovery"},{"key":"10.1016\/j.jmb.2024.168853_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.peptides.2021.170570","article-title":"Review of antiviral peptides for use against zoonotic and selected non-zoonotic viruses","volume":"142","author":"Hollmann","year":"2021","journal-title":"Peptides"},{"issue":"5","key":"10.1016\/j.jmb.2024.168853_b0030","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1016\/j.jmb.2007.03.073","article-title":"Inhibition of the activities of reverse transcriptase and integrase of human immunodeficiency virus type-1 by peptides derived from the homologous viral protein R (Vpr)","volume":"369","author":"Gleenberg","year":"2007","journal-title":"J. Mol. Biol."},{"issue":"18","key":"10.1016\/j.jmb.2024.168853_b0035","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":"2021","journal-title":"Curr. Pharm. Des."},{"issue":"22","key":"10.1016\/j.jmb.2024.168853_b0040","doi-asserted-by":"crossref","first-page":"5743","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."},{"issue":"W1","key":"10.1016\/j.jmb.2024.168853_b0045","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."},{"issue":"4","key":"10.1016\/j.jmb.2024.168853_b0050","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac265","article-title":"iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model","volume":"23","author":"Kurata","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"5","key":"10.1016\/j.jmb.2024.168853_b0055","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/17460441.2022.2050693","article-title":"Antimicrobial peptides: A plausible approach for COVID-19 treatment","volume":"17","author":"Rani","year":"2022","journal-title":"Expert Opin. Drug Discov."},{"key":"10.1016\/j.jmb.2024.168853_b0060","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.3389\/fimmu.2020.02030","article-title":"Antimicrobial peptides as potential antiviral factors in insect antiviral immune response","volume":"11","author":"Feng","year":"2020","journal-title":"Front. Immunol."},{"issue":"9","key":"10.1016\/j.jmb.2024.168853_b0065","doi-asserted-by":"crossref","DOI":"10.1099\/jgv.0.001661","article-title":"Natural antimicrobial peptides as a source of new antiviral agents","volume":"102","author":"Zakaryan","year":"2021","journal-title":"J. Gen. Virol."},{"issue":"6","key":"10.1016\/j.jmb.2024.168853_b0070","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","volume":"22","author":"Timmons","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.jmb.2024.168853_b0075","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/TCBB.2022.3183018","article-title":"PreTP-Stack: prediction of therapeutic peptide based on the stacked ensemble learning","volume":"20","author":"Yan","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"10","key":"10.1016\/j.jmb.2024.168853_b0080","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."},{"issue":"6","key":"10.1016\/j.jmb.2024.168853_b0085","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab209","article-title":"iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types","volume":"22","author":"Xiao","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"6","key":"10.1016\/j.jmb.2024.168853_b0090","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","volume":"22","author":"Pang","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0095","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s12859-024-05726-5","article-title":"Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model","volume":"25","author":"Akbar","year":"2024","journal-title":"BMC Bioinf."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0100","doi-asserted-by":"crossref","first-page":"13722","DOI":"10.1038\/s41598-023-40922-y","article-title":"A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines","volume":"13","author":"Singh","year":"2023","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.jmb.2024.168853_b0105","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae037","article-title":"FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization","volume":"25","author":"Jiang","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168853_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105909","article-title":"AntiCVP-Deep: Identify anti-coronavirus peptides between different negative datasets based on self-attention and deep learning","volume":"90","author":"Yan","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"10.1016\/j.jmb.2024.168853_b0115","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1002\/med.21658","article-title":"Machine intelligence in peptide therapeutics: A next-generation tool for rapid disease screening","volume":"40","author":"Basith","year":"2020","journal-title":"Med. Res. Rev."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0120","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 Bioinf. J."},{"issue":"5","key":"10.1016\/j.jmb.2024.168853_b0125","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"},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0130","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s12859-024-05744-3","article-title":"DPI_CDF: druggable protein identifier using cascade deep forest","volume":"25","author":"Arif","year":"2024","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168853_b0135","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.ymeth.2024.08.001","article-title":"StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features","volume":"230","author":"Arif","year":"2024","journal-title":"Methods"},{"key":"10.1016\/j.jmb.2024.168853_b0140","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.ymeth.2024.08.005","article-title":"A novel deep learning identifier for promoters and their strength using heterogeneous features","volume":"230","author":"Amjad","year":"2024","journal-title":"Methods"},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0145","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","volume":"273","author":"Chou","year":"2011","journal-title":"J. Theor. Biol."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0150","doi-asserted-by":"crossref","first-page":"7697","DOI":"10.1038\/s41598-022-11897-z","article-title":"AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning","volume":"12","author":"Charoenkwan","year":"2022","journal-title":"Sci. Rep."},{"issue":"6","key":"10.1016\/j.jmb.2024.168853_b0155","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","volume":"22","author":"Charoenkwan","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0160","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1186\/s12859-023-05463-1","article-title":"TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides","volume":"24","author":"Charoenkwan","year":"2023","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.jmb.2024.168853_b0165","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ymeth.2021.12.001","article-title":"StackDPPIV: A novel computational approach for accurate prediction of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides","volume":"204","author":"Charoenkwan","year":"2022","journal-title":"Methods"},{"key":"10.1016\/j.jmb.2024.168853_b0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105700","article-title":"NEPTUNE: a novel computational approach for accurate and large-scale identification of tumor homing peptides","volume":"148","author":"Charoenkwan","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"14","key":"10.1016\/j.jmb.2024.168853_b0175","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: a python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168853_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107386","article-title":"ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information","volume":"165","author":"Basith","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.jmb.2024.168853_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107848","article-title":"Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach","volume":"169","author":"Harun-Or-Roshid","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0190","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s13321-023-00721-z","article-title":"DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists","volume":"15","author":"Schaduangrat","year":"2023","journal-title":"J. Cheminform."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0195","doi-asserted-by":"crossref","first-page":"22994","DOI":"10.1038\/s41598-023-50393-w","article-title":"StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ER\u03b1 and ER\u03b2 antagonists","volume":"13","author":"Schaduangrat","year":"2023","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.jmb.2024.168853_b0200","doi-asserted-by":"crossref","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."},{"issue":"5","key":"10.1016\/j.jmb.2024.168853_b0205","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae305","article-title":"DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm","volume":"40","author":"Ullah","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jmb.2024.168853_b0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102860","article-title":"iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks","volume":"151","author":"Akbar","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0215","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1186\/s12859-024-05884-6","article-title":"StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning","volume":"25","author":"Rukh","year":"2024","journal-title":"BMC Bioinf."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0220","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3390\/ijms21010075","article-title":"iQSP: a sequence-based tool for the prediction and analysis of quorum sensing peptides using informative physicochemical properties","volume":"21","author":"Charoenkwan","year":"2019","journal-title":"Int. J. Mol. Sci."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0225","doi-asserted-by":"crossref","first-page":"16435","DOI":"10.1038\/s41598-022-20143-5","article-title":"StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy","volume":"12","author":"Schaduangrat","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.jmb.2024.168853_b0230","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s10822-021-00418-1","article-title":"StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors","volume":"35","author":"Malik","year":"2021","journal-title":"J. Comput. Aided Mol. Des."},{"issue":"3","key":"10.1016\/j.jmb.2024.168853_b0235","doi-asserted-by":"crossref","first-page":"EL140","DOI":"10.1121\/1.4865840","article-title":"Estimating confidence intervals for information transfer analysis of confusion matrices","volume":"135","author":"Azadpour","year":"2014","journal-title":"J. Acoust. Soc. Am."},{"issue":"4","key":"10.1016\/j.jmb.2024.168853_b0240","doi-asserted-by":"crossref","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."},{"issue":"4","key":"10.1016\/j.jmb.2024.168853_b0245","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa275","article-title":"Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework","volume":"22","author":"Wei","year":"2021","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0250","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","author":"Van Der Maaten","year":"2014","journal-title":"J. Mach. Learn. Res."},{"issue":"11","key":"10.1016\/j.jmb.2024.168853_b0255","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"10.1016\/j.jmb.2024.168853_b0260","doi-asserted-by":"crossref","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"},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0265","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1111\/j.0105-2896.2004.0124.x","article-title":"Anti-microbial peptides: from invertebrates to vertebrates","volume":"198","author":"Bulet","year":"2004","journal-title":"Immunol. Rev."},{"issue":"3","key":"10.1016\/j.jmb.2024.168853_b0270","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae208","article-title":"A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy","volume":"25","author":"Guan","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0275","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":"10.1016\/j.jmb.2024.168853_b0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2021.107588","article-title":"Prediction for understanding the effectiveness of antiviral peptides","volume":"95","author":"Nath","year":"2021","journal-title":"Comput. Biol. Chem."},{"issue":"5","key":"10.1016\/j.jmb.2024.168853_b0285","article-title":"Cationic antimicrobial peptides and their multifunctional role in the immune system","volume":"20","author":"Scott","year":"2000","journal-title":"Crit. Rev.\u2122 Immunol."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0290","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009736","article-title":"Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences","volume":"18","author":"Dickinson","year":"2022","journal-title":"PLoS Comput. Biol."},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0295","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s10989-020-10072-0","article-title":"Antiviral peptides: identification and validation","volume":"27","author":"Agarwal","year":"2021","journal-title":"Int. J. Pept. Res. Ther."},{"issue":"4","key":"10.1016\/j.jmb.2024.168853_b0300","doi-asserted-by":"crossref","first-page":"820","DOI":"10.3390\/v15040820","article-title":"DRAVP: a comprehensive database of antiviral peptides and proteins","volume":"15","author":"Liu","year":"2023","journal-title":"Viruses"},{"issue":"1","key":"10.1016\/j.jmb.2024.168853_b0305","first-page":"11","article-title":"CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning","volume":"21","author":"Qiang","year":"2020","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.jmb.2024.168853_b0310","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jbi.2017.09.011","article-title":"Computational prediction of therapeutic peptides based on graph index","volume":"75","author":"Xu","year":"2017","journal-title":"J. Biomed. Inform."}],"container-title":["Journal of Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022283624004832?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022283624004832?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T18:37:25Z","timestamp":1741027045000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022283624004832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":62,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["S0022283624004832"],"URL":"https:\/\/doi.org\/10.1016\/j.jmb.2024.168853","relation":{},"ISSN":["0022-2836"],"issn-type":[{"value":"0022-2836","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Stack-AVP: A Stacked Ensemble Predictor Based on Multi-view Information for Fast and Accurate Discovery of Antiviral Peptides","name":"articletitle","label":"Article Title"},{"value":"Journal of Molecular Biology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jmb.2024.168853","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"168853"}}