{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:17:35Z","timestamp":1772252255535,"version":"3.50.1"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005320","name":"Xidian University","doi-asserted-by":"publisher","award":["TZJH2024028"],"award-info":[{"award-number":["TZJH2024028"]}],"id":[{"id":"10.13039\/501100005320","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2024JC-YBMS-004"],"award-info":[{"award-number":["2024JC-YBMS-004"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biophysical Chemistry"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.bpc.2026.107591","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:55:22Z","timestamp":1770252922000},"page":"107591","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["iAFP-fLRM: Accurate identification of antifungal peptides via hybrid deep learning architecture and multi-modal feature fusion"],"prefix":"10.1016","volume":"332","author":[{"given":"Shengli","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.bpc.2026.107591_bb0005","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3390\/jof3040057","article-title":"Global and multi-national prevalence of fungal diseases-estimate precision","volume":"3","author":"Bongomin","year":"2017","journal-title":"J Fungi (Basel)."},{"issue":"Suppl. 1","key":"10.1016\/j.bpc.2026.107591_bb0010","doi-asserted-by":"crossref","first-page":"i5","DOI":"10.1093\/jac\/dki218","article-title":"Changing patterns and trends in systemic fungal infections","volume":"56","author":"Richardson","year":"2005","journal-title":"J. Antimicrob. Chemother."},{"issue":"2","key":"10.1016\/j.bpc.2026.107591_bb0015","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/S1473-3099(10)70218-8","article-title":"Emerging opportunistic yeast infections","volume":"11","author":"Miceli","year":"2011","journal-title":"Lancet Infect. Dis."},{"issue":"165","key":"10.1016\/j.bpc.2026.107591_bb0020","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.3004404","article-title":"Hidden killers: human fungal infections","volume":"4","author":"Brown","year":"2012","journal-title":"Sci. Transl. Med."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0025","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s12929-023-00926-2","article-title":"Diagnosis of invasive fungal infections: challenges and recent developments","volume":"30","author":"Fang","year":"2023","journal-title":"J. Biomed. Sci."},{"issue":"6390","key":"10.1016\/j.bpc.2026.107591_bb0030","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1126\/science.aap7999","article-title":"Worldwide emergence of resistance to antifungal drugs challenges human health and food security","volume":"360","author":"Fisher","year":"2018","journal-title":"Science"},{"issue":"9","key":"10.1016\/j.bpc.2026.107591_bb0035","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nrd.2017.46","article-title":"The antifungal pipeline: a reality check","volume":"16","author":"Perfect","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"issue":"9","key":"10.1016\/j.bpc.2026.107591_bb0040","doi-asserted-by":"crossref","DOI":"10.1371\/journal.ppat.1002870","article-title":"Antifungal drug discovery: something old and something new","volume":"8","author":"Butts","year":"2012","journal-title":"PLoS Pathog."},{"key":"10.1016\/j.bpc.2026.107591_bb0045","doi-asserted-by":"crossref","first-page":"1511461","DOI":"10.3389\/fmicb.2024.1511461","article-title":"Antifungal peptides from living organisms","volume":"15","author":"Gong","year":"2024","journal-title":"Front. Microbiol."},{"issue":"11","key":"10.1016\/j.bpc.2026.107591_bb0050","doi-asserted-by":"crossref","first-page":"3438","DOI":"10.3390\/molecules26113438","article-title":"Activity and mechanism of action of antifungal peptides from microorganisms: a review","volume":"26","author":"Li","year":"2021","journal-title":"Molecules"},{"issue":"10","key":"10.1016\/j.bpc.2026.107591_bb0055","doi-asserted-by":"crossref","DOI":"10.1371\/journal.ppat.1001067","article-title":"Antimicrobial peptides: primeval molecules or future drugs?","volume":"6","author":"Peters","year":"2010","journal-title":"PLoS Pathog."},{"issue":"2","key":"10.1016\/j.bpc.2026.107591_bb0060","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1002\/bip.20370","article-title":"Solution structures of stomoxyn and spinigerin, two insect antimicrobial peptides with an alpha-helical conformation","volume":"81","author":"Landon","year":"2006","journal-title":"Biopolymers"},{"issue":"32","key":"10.1016\/j.bpc.2026.107591_bb0065","doi-asserted-by":"crossref","first-page":"25000","DOI":"10.1007\/s11356-017-0179-8","article-title":"Isolation and identification of antifungal peptides from bacillus amyloliquefaciens W10","volume":"24","author":"Zhang","year":"2017","journal-title":"Environ. Sci. Pollut. Res. Int."},{"issue":"9","key":"10.1016\/j.bpc.2026.107591_bb0070","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1093\/abbs\/gmv071","article-title":"Isolation, characterization and mechanism of action of an antimicrobial peptide from Lecythis pisonis seeds with inhibitory activity against Candida albicans","volume":"47","author":"Vieira","year":"2015","journal-title":"Acta Biochim. Biophys. Sin. Shanghai"},{"issue":"4","key":"10.1016\/j.bpc.2026.107591_bb0075","doi-asserted-by":"crossref","first-page":"4021","DOI":"10.3390\/ijms13044021","article-title":"Isolation and purification of a novel deca-antifungal peptide from potato (Solanum tuberosum L. cv. Jopung) against Candida albicans","volume":"13","author":"Lee","year":"2012","journal-title":"Int. J. Mol. Sci."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0080","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s10930-012-9456-z","article-title":"Isolation, characterization and antifungal activity of proteinase inhibitors from Capsicum chinense Jacq. Seeds","volume":"32","author":"Dias","year":"2013","journal-title":"Protein J."},{"key":"10.1016\/j.bpc.2026.107591_bb0085","doi-asserted-by":"crossref","DOI":"10.3389\/fcimb.2021.743346","article-title":"Characterization, biological activity, and mechanism of action of a plant-based novel antifungal peptide, cc-AFP1, isolated from Carum carvi","volume":"11","author":"Seyedjavadi","year":"2021","journal-title":"Front. Cell. Infect. Microbiol."},{"issue":"8","key":"10.1016\/j.bpc.2026.107591_bb0090","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.3390\/ph6081055","article-title":"Characterization of antimicrobial peptides toward the development of novel antibiotics","volume":"6","author":"Aoki","year":"2013","journal-title":"Pharmaceuticals (Basel)"},{"issue":"22","key":"10.1016\/j.bpc.2026.107591_bb0095","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1093\/bioinformatics\/btz260","article-title":"Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis","volume":"35","author":"Aguilera-Mendoza","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bpc.2026.107591_bb0100","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3389\/fmicb.2018.00323","article-title":"In silico approach for prediction of antifungal peptides","volume":"9","author":"Agrawal","year":"2018","journal-title":"Front. Microbiol."},{"issue":"0","key":"10.1016\/j.bpc.2026.107591_bb0105","doi-asserted-by":"crossref","first-page":"21","DOI":"10.2197\/ipsjtbio.12.21","article-title":"Prediction of antifungal peptides by deep learning with character embedding","volume":"12","author":"Fang","year":"2019","journal-title":"IPSJ Transact. Bioinforma."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0110","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.32604\/cmc.2025.061700","article-title":"XGBoost-liver: an intelligent integrated features approach for classifying liver diseases using ensemble XGBoost training model","volume":"83","author":"Noor","year":"2025","journal-title":"Comput. Mater. Continua"},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0115","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s12859-025-06101-8","article-title":"Deep-ProBind: binding protein prediction with transformer-based deep learning model","volume":"26","author":"Khan","year":"2025","journal-title":"BMC Bioinforma."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0120","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1186\/s12915-025-02433-2","article-title":"A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features","volume":"23","author":"Almusallam","year":"2025","journal-title":"BMC Biol."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0125","doi-asserted-by":"crossref","first-page":"35872","DOI":"10.1038\/s41598-025-19689-x","article-title":"Integrating AI in security information and event management for real time cyber defense","volume":"15","author":"Khan","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0130","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1186\/s12859-019-3178-6","article-title":"SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models","volume":"20","author":"Wang","year":"2019","journal-title":"BMC Bioinforma."},{"key":"10.1016\/j.bpc.2026.107591_bb0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2020.104214","article-title":"Deep-AntiFP: prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks","volume":"208","author":"Ahmad","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"10","key":"10.1016\/j.bpc.2026.107591_bb0140","doi-asserted-by":"crossref","DOI":"10.1002\/pro.4758","article-title":"DeepAFP: an effective computational framework for identifying antifungal peptides based on deep learning","volume":"32","author":"Yao","year":"2023","journal-title":"Protein Sci."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0145","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab422","article-title":"Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM","volume":"23","author":"Sharma","year":"2022","journal-title":"Brief. Bioinform."},{"issue":"2","key":"10.1016\/j.bpc.2026.107591_bb0150","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac008","article-title":"Accelerating the discovery of antifungal peptides using deep temporal convolutional networks","volume":"23","author":"Singh","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bpc.2026.107591_bb0155","doi-asserted-by":"crossref","first-page":"104516","DOI":"10.1016\/j.chemolab.2022.104516","article-title":"iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multiinformation fusion and ensemble learning approach","volume":"222","author":"Ahmad","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0160","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac606","article-title":"AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning","volume":"24","author":"Fang","year":"2023","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bpc.2026.107591_bb0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiolchem.2024.108240","article-title":"Cycle-ESM: generation-assisted classification of antifungal peptides using ESM protein language model","volume":"113","author":"Wang","year":"2024","journal-title":"Comput. Biol. Chem."},{"key":"10.1016\/j.bpc.2026.107591_bb0170","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":"18","key":"10.1016\/j.bpc.2026.107591_bb0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e37820","article-title":"MLAFP-XN: leveraging neural network model for development of antifungal peptide identification tool","volume":"10","author":"Sultan","year":"2024","journal-title":"Heliyon"},{"issue":"10","key":"10.1016\/j.bpc.2026.107591_bb0180","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1016\/j.biochi.2011.07.011","article-title":"Oxidative stress by antimicrobial peptide pleurocidin triggers apoptosis in Candida albicans","volume":"93","author":"Cho","year":"2011","journal-title":"Biochimie"},{"issue":"10 Pt A","key":"10.1016\/j.bpc.2026.107591_bb0185","article-title":"Antimicrobial peptide protonectin disturbs the membrane integrity and induces ROS production in yeast cells","volume":"1848","author":"Wang","year":"2015","journal-title":"Biochim. Biophys. Acta"},{"key":"10.1016\/j.bpc.2026.107591_bb0190","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Proces. Syst."},{"issue":"3","key":"10.1016\/j.bpc.2026.107591_bb0195","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1002\/prot.1035","article-title":"Prediction of protein cellular attributes using pseudo-amino acid composition","volume":"43","author":"Chou","year":"2001","journal-title":"Proteins"},{"issue":"22","key":"10.1016\/j.bpc.2026.107591_bb0200","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1073\/pnas.89.22.10915","article-title":"Amino acid substitution matrices from protein blocks","volume":"89","author":"Henikoff","year":"1992","journal-title":"Proc. Natl. Acad. Sci. USA"},{"issue":"8","key":"10.1016\/j.bpc.2026.107591_bb0205","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"4","key":"10.1016\/j.bpc.2026.107591_bb0210","doi-asserted-by":"crossref","first-page":"5314","DOI":"10.1109\/TPAMI.2022.3206148","article-title":"ResMLP: feedforward networks for image classification with data-efficient training","volume":"45","author":"Touvron","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bpc.2026.107591_bb0215","doi-asserted-by":"crossref","first-page":"24482","DOI":"10.1038\/srep24482","article-title":"DRAMP: a comprehensive data repository of antimicrobial peptides","volume":"6","author":"Fan","year":"2016","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bpc.2026.107591_bb0220","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"},{"issue":"13","key":"10.1016\/j.bpc.2026.107591_bb0225","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"issue":"14","key":"10.1016\/j.bpc.2026.107591_bb0230","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"},{"issue":"4","key":"10.1016\/j.bpc.2026.107591_bb0235","article-title":"GSHSite: exploiting an iteratively statistical method to identify s-glutathionylation sites with substrate specificity","volume":"10","author":"Chen","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.bpc.2026.107591_bb0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2020.103999","article-title":"DNNAce: prediction of prokaryote lysine acetylation sites through deep neural networks with multiinformation fusion","volume":"200","author":"Yu","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"2","key":"10.1016\/j.bpc.2026.107591_bb0245","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac037","article-title":"Adapt-Kcr: a novel deep learning framework for accurate prediction of lysine crotonylation sites based on learning embedding features and attention architecture","volume":"23","author":"Li","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.bpc.2026.107591_bb0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.margen.2019.100723","article-title":"T-distributed stochastic neighbor embedding (t-SNE): a tool for eco-physiological transcriptomic analysis","volume":"51","author":"Cieslak","year":"2020","journal-title":"Mar. Genomics"}],"container-title":["Biophysical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301462226000244?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0301462226000244?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T03:27:31Z","timestamp":1772249251000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0301462226000244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":50,"alternative-id":["S0301462226000244"],"URL":"https:\/\/doi.org\/10.1016\/j.bpc.2026.107591","relation":{},"ISSN":["0301-4622"],"issn-type":[{"value":"0301-4622","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"iAFP-fLRM: Accurate identification of antifungal peptides via hybrid deep learning architecture and multi-modal feature fusion","name":"articletitle","label":"Article Title"},{"value":"Biophysical Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bpc.2026.107591","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"107591"}}