{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T02:05:50Z","timestamp":1772244350431,"version":"3.50.1"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications have practical benefit in clinical settings. Conformal prediction offers a versatile framework for addressing these concerns by quantifying the uncertainty of predictive models. In this perspective review, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response as well as the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments.<\/jats:p>","DOI":"10.3389\/fbinf.2025.1507448","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T01:44:08Z","timestamp":1740361448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Reliable machine learning models in genomic medicine using conformal prediction"],"prefix":"10.3389","volume":"5","author":[{"given":"Christina","family":"Papangelou","sequence":"first","affiliation":[]},{"given":"Konstantinos","family":"Kyriakidis","sequence":"additional","affiliation":[]},{"given":"Pantelis","family":"Natsiavas","sequence":"additional","affiliation":[]},{"given":"Ioanna","family":"Chouvarda","sequence":"additional","affiliation":[]},{"given":"Andigoni","family":"Malousi","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2202.01315","article-title":"Approximating full conformal prediction at scale via influence functions","author":"Abad","year":"2022"},{"key":"B3","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41698-020-0122-1","article-title":"Machine learning approaches to drug response prediction: challenges and recent progress","volume":"4","author":"Adam","year":"2020","journal-title":"NPJ Precis. Oncol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s10916-019-1497-9","article-title":"A hybrid scheme for heart disease diagnosis using rough set and cuckoo search technique","volume":"44","author":"Ahmed P","year":"2020","journal-title":"J. Med. Syst."},{"key":"B4","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.xphs.2020.09.055","article-title":"Predicting with confidence: using conformal prediction in drug discovery","volume":"110","author":"Alvarsson","year":"2021","journal-title":"J. Pharm. Sci."},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2107.07511","article-title":"A gentle introduction to conformal prediction and distribution-free uncertainty quantification","author":"Angelopoulos","year":"2021"},{"key":"B6","doi-asserted-by":"crossref","DOI":"10.1002\/9781118763117","volume-title":"Introduction to imprecise probabilities","author":"Augustin","year":"2014"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4172\/2472-1654.100147","article-title":"The certainty of uncertainty in genomic medicine: managing the challenge","volume":"3","author":"Barlow-Stewart","year":"2018","journal-title":"J. Healthc. Commun."},{"key":"B8","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s42256-018-0004-1","article-title":"The need for uncertainty quantification in machine-assisted medical decision making","volume":"1","author":"Begoli","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"B9","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: a review for statisticians","volume":"112","author":"Blei","year":"2017","journal-title":"J. Am. Stat. Assoc."},{"key":"B10","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/s41467-024-55676-y","article-title":"Functional protein mining with conformal guarantees","volume":"16","author":"Boger","year":"2024","journal-title":"Nat. Commun."},{"key":"B11","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","article-title":"Integrating structured biological data by kernel maximum mean discrepancy","volume":"22","author":"Borgwardt","year":"2006","journal-title":"Bioinformatics"},{"key":"B12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13321-018-0325-4","article-title":"Large scale comparison of qsar and conformal prediction methods and their applications in drug discovery","volume":"11","author":"Bosc","year":"2019","journal-title":"J. cheminformatics"},{"key":"B13","first-page":"114","article-title":"Mondrian conformal regressors","volume-title":"Conformal and probabilistic prediction and applications","author":"Bostr\u00f6m","year":"2020"},{"key":"B14","first-page":"24","article-title":"Mondrian conformal predictive distributions","volume-title":"Conformal and probabilistic Prediction and applications (PMLR)","author":"Bostr\u00f6m","year":"2021"},{"key":"B15","doi-asserted-by":"publisher","first-page":"545","DOI":"10.7861\/clinmedicine.17-6-545","article-title":"The rise of the genome and personalised medicine","volume":"17","author":"Brittain","year":"2017","journal-title":"Clin. Med."},{"key":"B16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/COINS51742.2021.9524167","article-title":"Inductive conformal out-of-distribution detection based on adversarial autoencoders","volume-title":"2021 IEEE international conference on omni-layer intelligent systems (COINS)","author":"Cai","year":"2021"},{"key":"B17","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"Xgboost: a scalable tree boosting system","volume-title":"Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining","author":"Chen","year":"2016"},{"key":"B18","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1038\/s41551-022-00988-x","article-title":"Tackling prediction uncertainty in machine learning for healthcare","volume":"7","author":"Chua","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"B19","doi-asserted-by":"publisher","first-page":"e4497","DOI":"10.1002\/pro.4497","article-title":"Bepipred-3.0: improved b-cell epitope prediction using protein language models","volume":"31","author":"Clifford","year":"2022","journal-title":"Protein Sci."},{"key":"B20","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1101\/2022.11.10.515917","article-title":"Validation of predicted conformal intervals for prediction of human clinical pharmacokinetics","author":"Fagerholm","year":"2022","journal-title":"bioRxiv"},{"key":"B21","doi-asserted-by":"publisher","first-page":"e2204569119","DOI":"10.1073\/pnas.2204569119","article-title":"Conformal prediction under feedback covariate shift for biomolecular design","volume":"119","author":"Fannjiang","year":"2022","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"B22","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.tiv.2018.01.021","article-title":"Predicting skin sensitizers with confidence\u2014using conformal prediction to determine applicability domain of gard","volume":"48","author":"Forreryd","year":"2018","journal-title":"Toxicol. Vitro"},{"key":"B23","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1093\/comjnl\/bxl065","article-title":"Hedging predictions in machine learning","volume":"50","author":"Gammerman","year":"2007","journal-title":"Comput. J."},{"key":"B24","first-page":"2151","article-title":"Selectivenet: a deep neural network with an integrated reject option","volume-title":"International conference on machine learning","author":"Geifman","year":"2019"},{"key":"B25","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1038\/ncomms1814","article-title":"Reliable detection of subclonal single-nucleotide variants in tumour cell populations","volume":"3","author":"Gerstung","year":"2012","journal-title":"Nat. Commun."},{"key":"B26","article-title":"Training machine learning-based qsar models with conformal prediction on experimental data from dna-encoded chemical libraries","author":"Geylan","year":"2021"},{"key":"B27","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1093\/jeea\/jvab023","article-title":"Precise or imprecise probabilities? evidence from survey response related to late-onset dementia","volume":"20","author":"Giustinelli","year":"2022","journal-title":"J. Eur. Econ. Assoc."},{"key":"B28","doi-asserted-by":"publisher","first-page":"fuad004","DOI":"10.1093\/femsre\/fuad004","article-title":"A guide to current methodology and usage of reverse vaccinology towards in silico vaccine discovery","volume":"47","author":"Goodswen","year":"2023","journal-title":"FEMS Microbiol. Rev."},{"key":"B29","doi-asserted-by":"publisher","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"key":"B30","volume-title":"Monte Carlo methods","author":"Hammersley","year":"2013"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.15.585269","article-title":"Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts","author":"Hernandez-Hernandez","year":"2024","journal-title":"bioRxiv"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1038\/s41467-024-52980-5","article-title":"Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones","author":"Ianevski","year":"2023","journal-title":"bioRxiv"},{"key":"B33","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with alphafold","volume":"596","author":"Jumper","year":"2021","journal-title":"nature"},{"key":"B34","doi-asserted-by":"publisher","first-page":"2326","DOI":"10.1109\/jbhi.2024.3350082","article-title":"A novel dual layer cascade reliability framework for an informed and intuitive clinician-ai interaction in diagnosis of colorectal cancer polyps","volume":"28","author":"Kapuria","year":"2024","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"B35","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1007\/s10462-017-9610-2","article-title":"Adaptive network based fuzzy inference system (anfis) training approaches: a comprehensive survey","volume":"52","author":"Karaboga","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"B36","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/futurepharmacol2010003","article-title":"Unraveling drug response from pharmacogenomic data to advance systems pharmacology decisions in tumor therapeutics","volume":"2","author":"Kardamiliotis","year":"2022","journal-title":"Future Pharmacol."},{"key":"B37","article-title":"Empirically validating conformal prediction on modern vision architectures under distribution shift and long-tailed data","author":"Kasa","year":"2023"},{"key":"B38","doi-asserted-by":"publisher","first-page":"2580","DOI":"10.1038\/s41467-020-16310-9","article-title":"A biochemically-interpretable machine learning classifier for microbial gwas","volume":"11","author":"Kavvas","year":"2020","journal-title":"Nat. Commun."},{"key":"B39","doi-asserted-by":"publisher","first-page":"e0017921","DOI":"10.1128\/cmr.00179-21","article-title":"Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective","volume":"35","author":"Kim","year":"2022","journal-title":"Clin. Microbiol. Rev."},{"key":"B40","doi-asserted-by":"publisher","first-page":"4823","DOI":"10.1021\/acs.jcim.1c00549","article-title":"Critical assessment of conformal prediction methods applied in binary classification settings","volume":"61","author":"Krstajic","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"B41","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1182\/blood.2019003535","article-title":"Targeted sequencing in dlbcl, molecular subtypes, and outcomes: a haematological malignancy research network report","volume":"135","author":"Lacy","year":"2020","journal-title":"Blood, J. Am. Soc. Hematol."},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2310.12033","article-title":"Codrug: conformal drug property prediction with density estimation under covariate shift","volume":"36","author":"Laghuvarapu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1612.01474","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017","journal-title":"Adv. neural Inf. Process. Syst."},{"key":"B44","article-title":"Reliable anti-cancer drug sensitivity prediction and prioritization","author":"Lenhof","year":"2023"},{"key":"B45","doi-asserted-by":"publisher","first-page":"12303","DOI":"10.1038\/s41598-024-62956-6","article-title":"Reliable anti-cancer drug sensitivity prediction and prioritization","volume":"14","author":"Lenhof","year":"2024","journal-title":"Sci. Rep."},{"key":"B46","first-page":"545","article-title":"Improving trustworthiness of ai disease severity rating in medical imaging with ordinal conformal prediction sets","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"Lu","year":""},{"key":"B47","doi-asserted-by":"publisher","first-page":"12008","DOI":"10.1609\/aaai.v36i11.21459","article-title":"Fair conformal predictors for applications in medical imaging","volume":"36","author":"Lu","year":"","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B48","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13321-024-00870-9","article-title":"Cpsign-conformal prediction for cheminformatics modeling","volume":"16","author":"McShane","year":"2023","journal-title":"J. Cheminform."},{"key":"B49","first-page":"198","article-title":"Pitfalls of conformal predictions for medical image classification","volume-title":"International workshop on uncertainty for safe utilization of machine learning in medical imaging","author":"Mehrtens","year":"2023"},{"key":"B50","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/gm463","article-title":"Peripheral blood derived gene panels predict response to infliximab in rheumatoid arthritis and crohn\u2019s disease","volume":"5","author":"Mesko","year":"2013","journal-title":"Genome Med."},{"key":"B51","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/1755-8794-3-15","article-title":"Peripheral blood gene expression patterns discriminate among chronic inflammatory diseases and healthy controls and identify novel targets","volume":"3","author":"Mesko","year":"2010","journal-title":"BMC Med. genomics"},{"key":"B52","first-page":"991","article-title":"Uncertainty in breast cancer risk prediction: a conformal prediction study of race stratification","volume-title":"Studies in health technology and informatics","author":"Millar","year":"2024"},{"key":"B53","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/s0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis","volume":"399","author":"Murray","year":"2022","journal-title":"lancet"},{"key":"B54","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41598-017-18972-w","article-title":"Developing an in silico minimum inhibitory concentration panel test for klebsiella pneumoniae","volume":"8","author":"Nguyen","year":"2018","journal-title":"Sci. Rep."},{"key":"B55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/23270012.2022.2031324","article-title":"Predicting amazon customer reviews with deep confidence using deep learning and conformal prediction","volume":"9","author":"Norinder","year":"2022","journal-title":"J. Manag. Anal."},{"key":"B56","doi-asserted-by":"publisher","first-page":"100129","DOI":"10.1016\/j.xgen.2022.100129","article-title":"Precisionfda truth challenge v2: calling variants from short and long reads in difficult-to-map regions","volume":"2","author":"Olson","year":"2022","journal-title":"Cell genomics"},{"key":"B57","doi-asserted-by":"publisher","first-page":"7761","DOI":"10.1038\/s41467-022-34945-8","article-title":"Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction","volume":"13","author":"Olsson","year":"2022","journal-title":"Nat. Commun."},{"key":"B58","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1093\/bioinformatics\/btaa119","article-title":"Vaxign-ml: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens","volume":"36","author":"Ong","year":"2020","journal-title":"Bioinformatics"},{"key":"B59","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1111\/bjh.15619","article-title":"Cell-of-origin in diffuse large b-cell lymphoma: findings from the UK\u2019s population-based haematological malignancy research network","volume":"185","author":"Painter","year":"2019","journal-title":"Br. J. Haematol."},{"key":"B60","doi-asserted-by":"crossref","DOI":"10.5772\/6078","article-title":"Inductive conformal prediction: theory and application to neural networks","volume-title":"Tools in artificial intelligence (Citeseer)","author":"Papadopoulos","year":"2008"},{"key":"B61","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1109\/access.2022.3233036","article-title":"Providing post-hoc explanation for node representation learning models through inductive conformal predictions","volume":"11","author":"Park","year":"2022","journal-title":"IEEE Access"},{"key":"B62","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1109\/tpami.2023.3327300","article-title":"Few-shot calibration of set predictors via meta-learned cross-validation-based conformal prediction","volume":"46","author":"Park","year":"2023","journal-title":"IEEE Trans. Pattern Analysis Mach. Intell."},{"key":"B63","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1080\/019697298125470","article-title":"Rough set theory and its applications to data analysis","volume":"29","author":"Pawlak","year":"1998","journal-title":"Cybern. and Syst."},{"key":"B64","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/tpami.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. pattern analysis Mach. Intell."},{"key":"B65","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/nbt.4235","article-title":"A universal snp and small-indel variant caller using deep neural networks","volume":"36","author":"Poplin","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"B66","doi-asserted-by":"publisher","first-page":"7695","DOI":"10.18653\/v1\/2024.findings-acl.459","article-title":"Cicle: conformal in-context learning for largescale multi-class food risk classification","author":"Randl","year":"2024"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2404.04287","article-title":"Conflare: conformal large language model retrieval","author":"Rouzrokh","year":"2024","journal-title":"arXiv Prepr. arXiv:2404.04287"},{"key":"B68","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s13073-015-0187-6","article-title":"Transferring genomics to the clinic: distinguishing burkitt and diffuse large b cell lymphomas","volume":"7","author":"Sha","year":"2015","journal-title":"Genome Med."},{"key":"B69","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1200\/jco.18.01314","article-title":"Molecular high-grade b-cell lymphoma: defining a poor-risk group that requires different approaches to therapy","volume":"37","author":"Sha","year":"2019","journal-title":"J. Clin. Oncol."},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.0706.3188","article-title":"A tutorial on conformal prediction","volume":"9","author":"Shafer","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"B71","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1109\/jbhi.2020.3032060","article-title":"Measuring domain shift for deep learning in histopathology","volume":"25","author":"Stacke","year":"2020","journal-title":"IEEE J. Biomed. health Inf."},{"key":"B72","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"Sugiyama","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"B73","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1038\/s41592-024-02184-y","article-title":"Tissue: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses","volume":"21","author":"Sun","year":"2024","journal-title":"Nat. Methods"},{"key":"B74","doi-asserted-by":"publisher","first-page":"5276","DOI":"10.1038\/s41467-021-25014-7","article-title":"Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction","volume":"12","author":"Sun","year":"2021","journal-title":"Nat. Commun."},{"key":"B75","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1904.06019","article-title":"Conformal prediction under covariate shift","volume":"32","author":"Tibshirani","year":"2019","journal-title":"Adv. neural Inf. Process. Syst."},{"key":"B76","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10472-017-9556-8","article-title":"Conformal prediction of biological activity of chemical compounds","volume":"81","author":"Toccaceli","year":"2017","journal-title":"Ann. Math. Artif. Intell."},{"key":"B77","doi-asserted-by":"publisher","first-page":"2024","DOI":"10.1101\/2024.03.20.585850","article-title":"Vaccinedesigner: a web-based tool for streamlined multi-epitope vaccine design","author":"Trygoniaris","year":"2024","journal-title":"bioRxiv"},{"key":"B78","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s41666-021-00113-8","article-title":"Conformal prediction in clinical medical sciences","volume":"6","author":"Vazquez","year":"2022","journal-title":"J. Healthc. Inf. Res."},{"key":"B79","doi-asserted-by":"publisher","first-page":"8657","DOI":"10.1007\/s11227-019-03132-w","article-title":"Modified adaptive neuro-fuzzy inference system (m-anfis) based multi-disease analysis of healthcare big data","volume":"76","author":"Vidhya","year":"2020","journal-title":"J. Supercomput."},{"key":"B80","volume-title":"Algorithmic learning in a random world","author":"Vovk","year":"2005"},{"key":"B81","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/jbhi.2020.2996300","article-title":"Deep learning with conformal prediction for hierarchical analysis of large-scale whole-slide tissue images","volume":"25","author":"Wieslander","year":"2020","journal-title":"IEEE J. Biomed. health Inf."},{"key":"B82","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1007\/s10489-019-01617-y","article-title":"Generalization of dempster\u2013shafer theory: a complex mass function","volume":"50","author":"Xiao","year":"2020","journal-title":"Appl. Intell."},{"key":"B83","doi-asserted-by":"publisher","first-page":"29478","DOI":"10.1021\/acsomega.4c02017","article-title":"Development and evaluation of conformal prediction methods for qsar","volume":"9","author":"Xu","year":"2023","journal-title":"ACS Omega"},{"key":"B84","doi-asserted-by":"publisher","first-page":"2648","DOI":"10.1021\/acs.jcim.1c00208","article-title":"Deep learning-based conformal prediction of toxicity","volume":"61","author":"Zhang","year":"2021","journal-title":"J. Chem. Inf. Model."}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2025.1507448\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T01:44:17Z","timestamp":1740361457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2025.1507448\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,24]]},"references-count":84,"alternative-id":["10.3389\/fbinf.2025.1507448"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2025.1507448","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.09.09.24312995","asserted-by":"object"}]},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,24]]},"article-number":"1507448"}}