{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:44:06Z","timestamp":1759365846125,"version":"build-2065373602"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2221-E-845-007","NSTC 114-2221-E-845-004"],"award-info":[{"award-number":["NSTC 113-2221-E-845-007","NSTC 114-2221-E-845-004"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-025-06254-6","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T10:03:35Z","timestamp":1759313015000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HEPAD: enhancing hemolytic peptide prediction with adaptive feature engineering and diverse sequence descriptors"],"prefix":"10.1186","volume":"26","author":[{"given":"Sih-Han","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jen-Chieh","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yi-Hsiang","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Shao-Chun","family":"Kuo","sequence":"additional","affiliation":[]},{"given":"Kuan","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Ching-Tai","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"6254_CR1","doi-asserted-by":"publisher","DOI":"10.26717\/BJSTR.2018.08.001694","author":"YA Haggag","year":"2018","unstructured":"Haggag YA. Peptides as drug candidates: limitations and recent development perspectives. Biomed J Sci Tech Res. 2018;8(4). https:\/\/doi.org\/10.26717\/BJSTR.2018.08.001694","journal-title":"Biomed J Sci Tech Res"},{"key":"6254_CR2","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.drudis.2009.10.009","volume":"15","author":"P Vlieghe","year":"2010","unstructured":"Vlieghe P, Lisowski V, Martinez J, Khrestchatisky M. Synthetic therapeutic peptides: science and market. Drug Discov Today. 2010;15:40\u201356.","journal-title":"Drug Discov Today"},{"key":"6254_CR3","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.coph.2008.06.002","volume":"8","author":"D Mcgregor","year":"2008","unstructured":"Mcgregor D. Discovering and improving novel peptide therapeutics. Curr Opin Pharmacol. 2008;8:616\u20139.","journal-title":"Curr Opin Pharmacol"},{"key":"6254_CR4","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.tibtech.2011.05.001","volume":"29","author":"LT Nguyen","year":"2011","unstructured":"Nguyen LT, Haney EF, Vogel HJ. The expanding scope of antimicrobial peptide structures and their modes of action. Trends Biotechnol. 2011;29:464\u201372.","journal-title":"Trends Biotechnol"},{"key":"6254_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41392-021-00710-4","volume":"7","author":"L Wang","year":"2022","unstructured":"Wang L, Wang N, Zhang W, Cheng X, Yan Z, Shao G, et al. Therapeutic peptides: current applications and future directions. Signal Transduct Target Ther. 2022;7:1\u201327.","journal-title":"Signal Transduct Target Ther"},{"key":"6254_CR6","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.3390\/ph6121543","volume":"6","author":"A Bahar","year":"2013","unstructured":"Bahar A, Ren D, Antimicrobial Peptides. Pharmaceuticals. 2013;6:1543\u201375.","journal-title":"Pharmaceuticals"},{"key":"6254_CR7","doi-asserted-by":"publisher","first-page":"6537","DOI":"10.1021\/acs.jcim.3c01563","volume":"63","author":"A Raza","year":"2023","unstructured":"Raza A, Uddin J, Almuhaimeed A, Akbar S, Zou Q, Ahmad A. AIPs-SnTCN: predicting anti-inflammatory peptides using fasttext and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks. J Chem Inf Model. 2023;63:6537\u201354.","journal-title":"J Chem Inf Model"},{"key":"6254_CR8","doi-asserted-by":"publisher","first-page":"btae305","DOI":"10.1093\/bioinformatics\/btae305","volume":"40","author":"M Ullah","year":"2024","unstructured":"Ullah M, Akbar S, Raza A, Zou Q. DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm. Bioinformatics. 2024;40:btae305.","journal-title":"Bioinformatics"},{"key":"6254_CR9","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s12859-024-05726-5","volume":"25","author":"S Akbar","year":"2024","unstructured":"Akbar S, Raza A, Zou Q. Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model. BMC Bioinformatics. 2024;25:102.","journal-title":"BMC Bioinformatics"},{"key":"6254_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102860","volume":"151","author":"S Akbar","year":"2024","unstructured":"Akbar S, Zou Q, Raza A, Alarfaj FK. iAFPs-Mv-BiTCN: predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks. Artif Intell Med. 2024;151: 102860.","journal-title":"Artif Intell Med"},{"key":"6254_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104682","volume":"230","author":"S Akbar","year":"2022","unstructured":"Akbar S, Ali F, Hayat M, Ahmad A, Khan S, Gul S. Prediction of antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy. Chemom Intell Lab Syst. 2022;230: 104682.","journal-title":"Chemom Intell Lab Syst"},{"key":"6254_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpharm.2020.119491","volume":"587","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Zhang H, Ghosh D, Williams RO. Just how prevalent are peptide therapeutic products?? A critical review. Int J Pharm. 2020;587: 119491.","journal-title":"Int J Pharm"},{"key":"6254_CR13","doi-asserted-by":"publisher","first-page":"3210","DOI":"10.1021\/acs.chemrev.9b00472","volume":"120","author":"RJ Malonis","year":"2020","unstructured":"Malonis RJ, Lai JR, Vergnolle O. Peptide-based vaccines: current progress and future challenges. Chem Rev. 2020;120:3210\u201329.","journal-title":"Chem Rev"},{"key":"6254_CR14","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1128\/AAC.49.1.388-397.2005","volume":"49","author":"Q Li","year":"2005","unstructured":"Li Q, Dong C, Deng A, Katsumata M, Nakadai A, Kawada T, et al. Hemolysis of erythrocytes by granulysin-derived peptides but not by granulysin. Antimicrob Agents Chemother. 2005;49:388\u201397.","journal-title":"Antimicrob Agents Chemother"},{"key":"6254_CR15","doi-asserted-by":"publisher","first-page":"22843","DOI":"10.1038\/srep22843","volume":"6","author":"K Chaudhary","year":"2016","unstructured":"Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, et al. A web server and mobile app for computing hemolytic potency of peptides. Sci Rep. 2016;6:22843.","journal-title":"Sci Rep"},{"key":"6254_CR16","doi-asserted-by":"publisher","first-page":"275","DOI":"10.4155\/fmc-2016-0188","volume":"9","author":"TS Win","year":"2017","unstructured":"Win TS, Malik AA, Prachayasittikul V, Wikberg S, Nantasenamat JE, Shoombuatong C. Hemopred: a web server for predicting the hemolytic activity of peptides. Future Med Chem. 2017;9:275\u201391.","journal-title":"Future Med Chem"},{"key":"6254_CR17","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1093\/nar\/28.1.374","volume":"28","author":"S Kawashima","year":"2000","unstructured":"Kawashima S, Kanehisa M, AAindex. Amino acid index database. Nucleic Acids Res. 2000;28:374.","journal-title":"Nucleic Acids Res"},{"key":"6254_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-67701-3","volume":"10","author":"PB Timmons","year":"2020","unstructured":"Timmons PB, Hewage CM. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Sci Rep. 2020;10:1\u201318.","journal-title":"Sci Rep"},{"key":"6254_CR19","doi-asserted-by":"publisher","first-page":"3350","DOI":"10.1093\/bioinformatics\/btaa160","volume":"36","author":"MM Hasan","year":"2020","unstructured":"Hasan MM, Schaduangrat N, Basith S, Lee G, Shoombuatong W, Manavalan B. Hlppred-fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics. 2020;36:3350\u20136.","journal-title":"Bioinformatics"},{"key":"6254_CR20","doi-asserted-by":"publisher","first-page":"D444","DOI":"10.1093\/nar\/gkt1008","volume":"42","author":"A Gautam","year":"2014","unstructured":"Gautam A, Chaudhary K, Singh S, Joshi A, Anand P, Tuknait A, et al. Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Res. 2014;42:D444\u20139.","journal-title":"Nucleic Acids Res"},{"key":"6254_CR21","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1093\/nar\/gkg095","volume":"31","author":"B Boeckmann","year":"2003","unstructured":"Boeckmann B, Bairoch A, Apweiler R, Blatter M-C, Estreicher A, Gasteiger E, et al. The SWISS-PROT protein knowledgebase and its supplement trembl in 2003. Nucleic Acids Res. 2003;31:365\u201370.","journal-title":"Nucleic Acids Res"},{"key":"6254_CR22","doi-asserted-by":"publisher","first-page":"D288","DOI":"10.1093\/nar\/gkaa991","volume":"49","author":"M Pirtskhalava","year":"2021","unstructured":"Pirtskhalava M, Amstrong AA, Grigolava M, Chubinidze M, Alimbarashvili E, Vishnepolsky B, et al. DBAASP v3: database of antimicrobial\/cytotoxic activity and structure of peptides as a resource for development of new therapeutics. Nucleic Acids Res. 2021;49:D288\u201397.","journal-title":"Nucleic Acids Res"},{"key":"6254_CR23","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273\u201397.","journal-title":"Mach Learn"},{"key":"6254_CR24","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189\u2013232.","journal-title":"Ann Stat"},{"key":"6254_CR25","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems; NIPS\u201917; Curran Associates Inc.: Red Hook, NY, USA, 2017;3149\u20133157."},{"key":"6254_CR26","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C, XGBoost:. A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. pp. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"6254_CR27","doi-asserted-by":"publisher","first-page":"12","DOI":"10.11613\/BM.2014.003","volume":"24","author":"S Sperandei","year":"2014","unstructured":"Sperandei S. Understanding logistic regression analysis. Biochem Med. 2014;24:12\u20138.","journal-title":"Biochem Med"},{"key":"6254_CR28","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1093\/bioinformatics\/btr291","volume":"27","author":"T-Y Lee","year":"2011","unstructured":"Lee T-Y, Lin Z-Q, Hsieh S-J, Breta\u00f1a NA, Lu C-T. Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences. Bioinformatics. 2011;27:1780\u20137.","journal-title":"Bioinformatics"},{"key":"6254_CR29","doi-asserted-by":"publisher","first-page":"109535","DOI":"10.1109\/ACCESS.2020.2999394","volume":"8","author":"J-N Sun","year":"2020","unstructured":"Sun J-N, Yang H-Y, Yao J, Ding H, Han S-G, Wu C-Y, et al. Prediction of cyclin protein using two-step feature selection technique. IEEE Access. 2020;8:109535\u201342.","journal-title":"IEEE Access"},{"key":"6254_CR30","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/1472-6807-7-25","volume":"7","author":"K Chen","year":"2007","unstructured":"Chen K, Kurgan LA, Ruan J. Prediction of flexible\/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol. 2007;7:25.","journal-title":"BMC Struct Biol"},{"key":"6254_CR31","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/prot.1035","volume":"43","author":"KC Chou","year":"2001","unstructured":"Chou KC. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins. 2001;43:246\u201355.","journal-title":"Proteins"},{"key":"6254_CR32","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.jmb.2011.02.053","volume":"408","author":"CN Pace","year":"2011","unstructured":"Pace CN, Fu H, Fryar KL, Landua J, Trevino SR, Shirley BA, et al. Contribution of hydrophobic interactions to protein stability. J Mol Biol. 2011;408:514\u201328.","journal-title":"J Mol Biol"},{"key":"6254_CR33","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1093\/bioinformatics\/bth466","volume":"21","author":"K-C Chou","year":"2005","unstructured":"Chou K-C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics. 2005;21:10\u20139.","journal-title":"Bioinformatics"},{"key":"6254_CR34","doi-asserted-by":"publisher","first-page":"552","DOI":"10.2174\/1573406413666170515120507","volume":"13","author":"L-M Liu","year":"2017","unstructured":"Liu L-M, Xu Y, Chou K-C. Ipgk-PseAAC: identify lysine phosphoglycerylation sites in proteins by incorporating four different tiers of amino acid pairwise coupling information into the general PseAAC. Med Chem. 2017;13:552\u20139.","journal-title":"Med Chem"},{"key":"6254_CR35","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1093\/bioinformatics\/btt196","volume":"29","author":"X Chen","year":"2013","unstructured":"Chen X, Qiu J-D, Shi S-P, Suo S-B, Huang S-Y, Liang R-P. Incorporating key position and amino acid residue features to identify general and species-specific ubiquitin conjugation sites. Bioinformatics. 2013;29:1614\u201322.","journal-title":"Bioinformatics"},{"key":"6254_CR36","doi-asserted-by":"crossref","unstructured":"Pande A, Patiyal S, Lathwal A, Arora C, Kaur D, Dhall A et al.Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models. J. Comput. Biol. 2023;30(2):204\u2013222.","DOI":"10.1089\/cmb.2022.0241"},{"key":"6254_CR37","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1089\/cmb.2022.0241","volume":"30","author":"A Pande","year":"2023","unstructured":"Pande A, Patiyal S, Lathwal A, Arora C, Kaur D, Dhall A, et al. Pfeature: a tool for computing wide range of protein features and building prediction models. J Comput Biol. 2023;30:204\u201322.","journal-title":"J Comput Biol"},{"key":"6254_CR38","doi-asserted-by":"publisher","first-page":"8700","DOI":"10.1073\/pnas.92.19.8700","volume":"92","author":"I Dubchak","year":"1995","unstructured":"Dubchak I, Muchnik I, Holbrook SR, Kim SH. Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci U S A. 1995;92:8700\u20134.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"6254_CR39","doi-asserted-by":"publisher","first-page":"4337","DOI":"10.1073\/pnas.0607879104","volume":"104","author":"J Shen","year":"2007","unstructured":"Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A. 2007;104:4337\u201341.","journal-title":"Proc Natl Acad Sci U S A"},{"key":"6254_CR40","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1089\/omi.2015.0095","volume":"19","author":"V Saravanan","year":"2015","unstructured":"Saravanan V, Gautham N. Harnessing computational biology for exact linear B-cell epitope prediction: a novel amino acid composition-based feature descriptor. OMICS. 2015;19:648\u201358.","journal-title":"OMICS"},{"key":"6254_CR41","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/bioinformatics\/btr110","volume":"27","author":"C Vens","year":"2011","unstructured":"Vens C, Rosso M-N, Danchin EGJ. Identifying discriminative classification-based motifs in biological sequences. Bioinformatics. 2011;27:1231\u20138.","journal-title":"Bioinformatics"},{"key":"6254_CR42","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"6254_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1\u201313.","journal-title":"J Stat Softw"},{"key":"6254_CR44","unstructured":"Moez A, PyCaret. An open source, low-code machine learning library in python. 2020. https:\/\/www.pycaret.org"},{"key":"6254_CR45","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M, Optuna. A Next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, NY, USA: Association for Computing Machinery; 2019. pp. 2623\u201331.","DOI":"10.1145\/3292500.3330701"},{"key":"6254_CR46","unstructured":"Bergstra J, Bardenet R, Bengio Y, K\u00e9gl B. Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc.; 2011. pp. 2546\u201354."},{"key":"6254_CR47","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29\u201336.","journal-title":"Radiology"},{"key":"6254_CR48","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.jmb.2006.09.020","volume":"366","author":"A Senes","year":"2007","unstructured":"Senes A, Chadi DC, Law PB, Walters RFS, Nanda V, DeGrado WF. Ez, a depth-dependent potential for assessing the energies of insertion of amino acid side-chains into membranes: derivation and applications to determining the orientation of transmembrane and interfacial helices. J Mol Biol. 2007;366:436\u201348.","journal-title":"J Mol Biol"},{"key":"6254_CR49","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1021\/jm9700575","volume":"41","author":"M Sandberg","year":"1998","unstructured":"Sandberg M, Eriksson L, Jonsson J, Sj\u00f6str\u00f6m M, Wold S. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem. 1998;41:2481\u201391.","journal-title":"J Med Chem"},{"key":"6254_CR50","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1021\/jm00390a003","volume":"30","author":"S Hellberg","year":"1987","unstructured":"Hellberg S, Sjoestroem M, Skagerberg B, Wold S. Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem. 1987;30:1126\u201335.","journal-title":"J Med Chem"},{"key":"6254_CR51","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc.; 2017. pp. 4768\u201377."},{"key":"6254_CR52","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"6254_CR53","first-page":"3221","volume":"15","author":"L van der Maaten","year":"2014","unstructured":"van der Maaten L. Accelerating t-SNE using Tree-Based algorithms. J Mach Learn Res. 2014;15:3221\u201345.","journal-title":"J Mach Learn Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06254-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06254-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06254-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T11:05:29Z","timestamp":1759316729000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06254-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6254"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06254-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"24 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"234"}}