{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T03:14:30Z","timestamp":1788146070048,"version":"build-2803163510"},"reference-count":178,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Funct Integr Genomics"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10142-026-02024-6","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T02:42:35Z","timestamp":1788144155000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integration of artificial intelligence and multi-omics for precision medicine"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0069-4212","authenticated-orcid":false,"given":"Hany E.","family":"Marei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Cenciarelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Vagni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2024_CR4","doi-asserted-by":"publisher","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. Proc 2016 ACM SIGSAC Conf Comput Commun Secur 308\u2013318. https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"2024_CR5","doi-asserted-by":"publisher","first-page":"1773","DOI":"10.1038\/s41591-022-01981-2","volume":"28","author":"JN Acosta","year":"2022","unstructured":"Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ (2022) Multimodal biomedical AI. Nat Med 28:1773\u20131784. https:\/\/doi.org\/10.1038\/s41591-022-01981-2","journal-title":"Nat Med"},{"key":"2024_CR6","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1038\/s41592-025-02772-6","volume":"22","author":"C Ahlmann-Eltze","year":"2025","unstructured":"Ahlmann-Eltze C, Huber W, Anders S (2025) Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Nat Methods 22:1657\u20131661. https:\/\/doi.org\/10.1038\/s41592-025-02772-6","journal-title":"Nat Methods"},{"key":"2024_CR7","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nrg.2016.59","volume":"17","author":"CD Allis","year":"2016","unstructured":"Allis CD, Jenuwein T (2016) The molecular hallmarks of epigenetic control. Nat Rev Genet 17:487\u2013500. https:\/\/doi.org\/10.1038\/nrg.2016.59","journal-title":"Nat Rev Genet"},{"key":"2024_CR8","doi-asserted-by":"publisher","first-page":"6562","DOI":"10.1073\/pnas.102102699","volume":"99","author":"C Ambroise","year":"2002","unstructured":"Ambroise C, McLachlan GJ (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc Natl Acad Sci USA 99:6562\u20136566. https:\/\/doi.org\/10.1073\/pnas.102102699","journal-title":"Proc Natl Acad Sci USA"},{"key":"2024_CR9","doi-asserted-by":"publisher","first-page":"n2281","DOI":"10.1136\/bmj.n2281","volume":"375","author":"CL Andaur Navarro","year":"2021","unstructured":"Andaur Navarro CL, Damen JAA, Takada T, Nijman SWJ, Dhiman P, Ma J, Collins GS, Bajpai R, Riley RD, Moons KGM, Hooft L (2021) Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ 375:n2281. https:\/\/doi.org\/10.1136\/bmj.n2281","journal-title":"BMJ"},{"key":"2024_CR10","doi-asserted-by":"publisher","first-page":"878","DOI":"10.15252\/msb.20156651","volume":"12","author":"C Angermueller","year":"2016","unstructured":"Angermueller C, P\u00e4rnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12:878. https:\/\/doi.org\/10.15252\/msb.20156651","journal-title":"Mol Syst Biol"},{"key":"2024_CR11","doi-asserted-by":"publisher","first-page":"5088","DOI":"10.3390\/app11115088","volume":"11","author":"AM Antoniadi","year":"2021","unstructured":"Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, Mooney C (2021) Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl Sci 11:5088. https:\/\/doi.org\/10.3390\/app11115088","journal-title":"Appl Sci"},{"key":"2024_CR12","doi-asserted-by":"publisher","first-page":"e8124","DOI":"10.15252\/msb.20178124","volume":"14","author":"R Argelaguet","year":"2018","unstructured":"Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W, Stegle O (2018) Multi-Omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 14:e8124. https:\/\/doi.org\/10.15252\/msb.20178124","journal-title":"Mol Syst Biol"},{"key":"2024_CR13","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1038\/s41592-023-01909-9","volume":"20","author":"T Ashuach","year":"2023","unstructured":"Ashuach T, Gabitto MI, Koodli RV, Saldi GA, Jordan MI, Yosef N (2023) MultiVI: deep generative model for the integration of multimodal data. Nat Methods 20:1222\u20131231. https:\/\/doi.org\/10.1038\/s41592-023-01909-9","journal-title":"Nat Methods"},{"key":"2024_CR14","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","volume":"18","author":"\u017d Avsec","year":"2021","unstructured":"Avsec \u017d, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A, Taylor KR, Assael Y, Jumper J, Kohli P, Kelley DR (2021) Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 18:1196\u20131203. https:\/\/doi.org\/10.1038\/s41592-021-01252-x","journal-title":"Nat Methods"},{"key":"2024_CR15","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1126\/science.abj8754","volume":"373","author":"M Baek","year":"2021","unstructured":"Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871\u2013876. https:\/\/doi.org\/10.1126\/science.abj8754","journal-title":"Science"},{"key":"2024_CR16","doi-asserted-by":"publisher","unstructured":"Beede E, Baylor E, Hersch F, Iurchenko A, Wilcox L, Ruamviboonsuk P, Vardoulakis LM (2020) A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proc 2020 CHI Conf Hum Factors Comput Syst 1\u201312. https:\/\/doi.org\/10.1145\/3313831.3376718","DOI":"10.1145\/3313831.3376718"},{"key":"2024_CR17","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1534\/genetics.118.301298","volume":"210","author":"P Bellot","year":"2018","unstructured":"Bellot P, de Los Campos G, P\u00e9rez-Enciso M (2018) Can deep learning improve genomic prediction of complex human traits? Genetics 210:809\u2013819. https:\/\/doi.org\/10.1534\/genetics.118.301298","journal-title":"Genetics"},{"key":"2024_CR18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1038\/s41576-018-0074-2","volume":"20","author":"M Berdasco","year":"2019","unstructured":"Berdasco M, Esteller M (2019) Clinical epigenetics: seizing opportunities for translation. Nat Rev Genet 20:109\u2013127. https:\/\/doi.org\/10.1038\/s41576-018-0074-2","journal-title":"Nat Rev Genet"},{"key":"2024_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-20059-5_1","volume-title":"Computer vision - ECCV 2022","author":"B Boecking","year":"2022","unstructured":"Boecking B, Usuyama N, Bannur S, Castro DC, Schwaighofer A, Hyland S, Wetscherek M, Naumann T, Nori A, Alvarez-Valle J et al (2022) Making the most of text semantics to improve biomedical vision-language processing. Computer vision - ECCV 2022. Lecture Notes in Computer Science, vol 13696. Springer, Cham, pp 1\u201321. https:\/\/doi.org\/10.1007\/978-3-031-20059-5_1"},{"key":"2024_CR20","unstructured":"Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, Arx Sv, Bernstein MS, Bohg J, Bosselut A, Brunskill E et al (2021) On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https:\/\/arxiv.org\/abs\/2108.07258. Accessed 29 July 2026"},{"key":"2024_CR21","doi-asserted-by":"publisher","unstructured":"Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. Proc 2017 ACM SIGSAC Conf Comput Commun Secur 1175\u20131191. https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"2024_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"2024_CR23","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1038\/s41467-020-20430-7","volume":"12","author":"L Cantini","year":"2021","unstructured":"Cantini L, Zakeri P, Hernandez C, Naldi A, Thieffry D, Remy E, Baudot A (2021) Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat Commun 12:124. https:\/\/doi.org\/10.1038\/s41467-020-20430-7","journal-title":"Nat Commun"},{"key":"2024_CR24","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/nature26000","volume":"555","author":"D Capper","year":"2018","unstructured":"Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, Koelsche C, Sahm F, Chavez L, Reuss DE et al (2018) DNA methylation-based classification of central nervous system tumours. Nature 555:469\u2013474. https:\/\/doi.org\/10.1038\/nature26000","journal-title":"Nature"},{"key":"2024_CR25","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1038\/s41586-019-1411-0","volume":"571","author":"G Cavalli","year":"2019","unstructured":"Cavalli G, Heard E (2019) Advances in epigenetics link genetics to the environment and disease. Nature 571:489\u2013499. https:\/\/doi.org\/10.1038\/s41586-019-1411-0","journal-title":"Nature"},{"key":"2024_CR26","unstructured":"Cawley GC, Talbot NLC (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. Journal of Machine Learning Research 11:2079\u20132107. https:\/\/jmlr.org\/papers\/v11\/cawley10a.html. Accessed 29 July 2026"},{"key":"2024_CR27","doi-asserted-by":"publisher","first-page":"e0176278","DOI":"10.1371\/journal.pone.0176278","volume":"12","author":"P Chalise","year":"2017","unstructured":"Chalise P, Fridley BL (2017) Integrative clustering of multi-level \u2018omic data based on non-negative matrix factorization algorithm. PLoS ONE 12:e0176278. https:\/\/doi.org\/10.1371\/journal.pone.0176278","journal-title":"PLoS ONE"},{"key":"2024_CR28","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1038\/s43018-025-00917-2","volume":"6","author":"TG Chang","year":"2025","unstructured":"Chang TG, Park S, Sch\u00e4ffer AA, Jiang P, Ruppin E (2025) Hallmarks of artificial intelligence contributions to precision oncology. Nat Cancer 6:417\u2013431. https:\/\/doi.org\/10.1038\/s43018-025-00917-2","journal-title":"Nat Cancer"},{"key":"2024_CR29","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York 785\u2013794. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2024_CR30","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.ccell.2022.07.004","volume":"40","author":"RJ Chen","year":"2022","unstructured":"Chen RJ, Lu MY, Williamson DFK, Chen TY, Lipkova J, Noor Z, Shaban M, Shady M, Williams M, Joo B, Mahmood F (2022) Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell 40:865\u2013878e6. https:\/\/doi.org\/10.1016\/j.ccell.2022.07.004","journal-title":"Cancer Cell"},{"key":"2024_CR31","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","volume":"110","author":"E Christodoulou","year":"2019","unstructured":"Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B (2019) A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 110:12\u201322. https:\/\/doi.org\/10.1016\/j.jclinepi.2019.02.004","journal-title":"J Clin Epidemiol"},{"key":"2024_CR32","doi-asserted-by":"publisher","first-page":"111606","DOI":"10.1016\/j.jclinepi.2024.111606","volume":"178","author":"M Colacci","year":"2025","unstructured":"Colacci M, Huang YQ, Postill G, Zhelnov P, Fennelly O, Verma A, Straus S, Tricco AC (2025) Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms. J Clin Epidemiol 178:111606. https:\/\/doi.org\/10.1016\/j.jclinepi.2024.111606","journal-title":"J Clin Epidemiol"},{"key":"2024_CR33","doi-asserted-by":"publisher","first-page":"55","DOI":"10.7326\/M14-0697","volume":"162","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162:55\u201363. https:\/\/doi.org\/10.7326\/M14-0697","journal-title":"Ann Intern Med"},{"key":"2024_CR34","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M et al (2024) TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https:\/\/doi.org\/10.1136\/bmj-2023-078378","journal-title":"BMJ"},{"key":"2024_CR35","doi-asserted-by":"publisher","unstructured":"Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, Silva TC, Groeneveld C, Wong CK, Cho SW et al (2018) The chromatin accessibility landscape of primary human cancers. Science 362. https:\/\/doi.org\/10.1126\/science.aav1898","DOI":"10.1126\/science.aav1898"},{"key":"2024_CR36","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1038\/nbt.2877","volume":"32","author":"JC Costello","year":"2014","unstructured":"Costello JC, Heiser LM, Georgii E, G\u00f6nen M, Menden MP, Wang NJ, Bansal M, Ammad-ud-din M, Hintsanen P, Khan SA, Mpindi JP, Kallioniemi O, Honkela A, Aittokallio T, Wennerberg K, Community NCIDREAM, Collins JJ, Gallahan D, Singer D, Saez-Rodriguez J et al (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32:1202\u20131212 [23 authors total]. https:\/\/doi.org\/10.1038\/nbt.2877","journal-title":"Nat Biotechnol"},{"key":"2024_CR37","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","volume":"26","author":"S Cruz Rivera","year":"2020","unstructured":"Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26:1351\u20131363. https:\/\/doi.org\/10.1038\/s41591-020-1037-7","journal-title":"Nat Med"},{"key":"2024_CR38","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","volume":"21","author":"H Cui","year":"2024","unstructured":"Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B (2024) scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 21:1470\u20131480. https:\/\/doi.org\/10.1038\/s41592-024-02201-0","journal-title":"Nat Methods"},{"key":"2024_CR2","doi-asserted-by":"publisher","first-page":"e10240","DOI":"10.15252\/msb.202110240","volume":"17","author":"J \u010cuklina","year":"2021","unstructured":"\u010cuklina J, Lee CH, Williams EG, Sajic T, Collins BC, Rodr\u00edguez Mart\u00ednez M, Sharma VS, Wendt F, Goetze S, Keele GR, Wollscheid B, Aebersold R (2021) Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial. Mol Syst Biol 17:e10240. https:\/\/doi.org\/10.15252\/msb.202110240","journal-title":"Mol Syst Biol"},{"key":"2024_CR39","doi-asserted-by":"publisher","first-page":"100945","DOI":"10.1016\/j.patter.2024.100945","volume":"5","author":"BP Danek","year":"2024","unstructured":"Danek BP, Makarious MB, Dadu A, Vitale D, Lee PS, Singleton AB, Nalls MA, Sun J, Faghri F (2024) Federated learning for multi-omics: A performance evaluation in Parkinson\u2019s disease. Patterns (N Y) 5:100945. https:\/\/doi.org\/10.1016\/j.patter.2024.100945","journal-title":"Patterns (N Y)"},{"key":"2024_CR40","doi-asserted-by":"publisher","first-page":"eabq6147","DOI":"10.1126\/sciadv.abq6147","volume":"8","author":"R Daneshjou","year":"2022","unstructured":"Daneshjou R, Vodrahalli K, Novoa RA, Jenkins M, Liang W, Rotemberg V, Ko J, Swetter SM, Bailey EE, Gevaert O et al (2022) Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv 8:eabq6147. https:\/\/doi.org\/10.1126\/sciadv.abq6147","journal-title":"Sci Adv"},{"key":"2024_CR41","doi-asserted-by":"publisher","first-page":"102067","DOI":"10.1016\/j.genrep.2024.102067","volume":"37","author":"S Dasgupta","year":"2024","unstructured":"Dasgupta S (2024) Identification and molecular modelling of potential drugs targeting the genes involved in the progression of lung cancer in patients with idiopathic pulmonary fibrosis. Gene Rep 37:102067. https:\/\/doi.org\/10.1016\/j.genrep.2024.102067","journal-title":"Gene Rep"},{"key":"2024_CR43","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1038\/s41592-026-03120-y","volume":"23","author":"A DenAdel","year":"2026","unstructured":"DenAdel A, Hughes M, Thoutam A, Gupta A, Navia AW, Fusi N, Raghavan S, Winter PS, Amini AP, Crawford L (2026) Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance. Nat Methods 23:1447\u20131457. https:\/\/doi.org\/10.1038\/s41592-026-03120-y","journal-title":"Nat Methods"},{"key":"2024_CR44","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s41512-022-00126-w","volume":"6","author":"P Dhiman","year":"2022","unstructured":"Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS (2022) Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagn Progn Res 6:13. https:\/\/doi.org\/10.1186\/s41512-022-00126-w","journal-title":"Diagn Progn Res"},{"key":"2024_CR45","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","volume":"20","author":"G Eraslan","year":"2019","unstructured":"Eraslan G, Avsec \u017d, Gagneur J, Theis FJ (2019) Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20:389\u2013403. https:\/\/doi.org\/10.1038\/s41576-019-0122-6","journal-title":"Nat Rev Genet"},{"key":"2024_CR47","unstructured":"European Parliament and Council of the European Union (2024a) Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828 (Artificial Intelligence Act). Official Journal of the European Union OJ L, 2024\/1689, 12.7.2024. http:\/\/data.europa.eu\/eli\/reg\/2024\/1689\/oj. Accessed 29 July 2026"},{"key":"2024_CR46","first-page":"176","volume":"117","author":"European Parliament and Council of the European Union","year":"2017","unstructured":"European Parliament and Council of the European Union (2017) Regulation (EU) 2017\/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices and repealing Directive 98\/79\/EC and Commission Decision 2010\/227\/EU. Official J Eur Union OJ L 117:176\u2013332. http:\/\/data.europa.eu\/eli\/reg\/2017\/746\/oj Accessed 29 July 2026","journal-title":"Official J Eur Union OJ L"},{"key":"2024_CR48","first-page":"972024","volume":"2024\/1860","author":"European Parliament and Council of the European Union","year":"2024","unstructured":"European Parliament and Council of the European Union (2024b) Regulation (EU) 2024\/1860 of the European Parliament and of the Council of 13 June 2024 amending Regulations (EU) 2017\/745 and (EU) 2017\/746 as regards a gradual roll-out of Eudamed, the obligation to inform in case of interruption or discontinuation of supply, and transitional provisions for certain in vitro diagnostic medical devices. Official J Eur Union OJ L 2024\/1860:972024. http:\/\/data.europa.eu\/eli\/reg\/2024\/1860\/oj Accessed 29 July 2026","journal-title":"Official J Eur Union OJ L"},{"key":"2024_CR49","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1056\/NEJMra1402513","volume":"378","author":"AP Feinberg","year":"2018","unstructured":"Feinberg AP (2018) The key role of epigenetics in human disease prevention and mitigation. N Engl J Med 378:1323\u20131334. https:\/\/doi.org\/10.1056\/NEJMra1402513","journal-title":"N Engl J Med"},{"key":"2024_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1\u201322. https:\/\/doi.org\/10.18637\/jss.v033.i01","journal-title":"J Stat Softw"},{"key":"2024_CR51","doi-asserted-by":"publisher","unstructured":"Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, Hayter JBR, Vickers R, Roberts C, Tang J et al (2021) Utilizing graph machine learning within drug discovery and development. Brief Bioinform 22. https:\/\/doi.org\/10.1093\/bib\/bbab159","DOI":"10.1093\/bib\/bbab159"},{"key":"2024_CR52","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1038\/s41592-019-0426-7","volume":"16","author":"S Gessulat","year":"2019","unstructured":"Gessulat S, Schmidt T, Zolg DP, Samaras P, Schnatbaum K, Zerweck J, Knaute T, Rechenberger J, Delanghe B, Huhmer A, Reimer U, Ehrlich HC, Aiche S, Kuster B, Wilhelm M (2019) Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 16:509\u2013518. https:\/\/doi.org\/10.1038\/s41592-019-0426-7","journal-title":"Nat Methods"},{"key":"2024_CR53","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi M, Oakden-Rayner L, Beam AL (2021) The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health 3:e745\u2013e750. https:\/\/doi.org\/10.1016\/S2589-7500(21)00208-9","journal-title":"Lancet Digit Health"},{"key":"2024_CR54","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1016\/j.fertnstert.2018.05.006","volume":"109","author":"LH Goetz","year":"2018","unstructured":"Goetz LH, Schork NJ (2018) Personalized medicine: motivation, challenges, and progress. Fertil Steril 109:952\u2013963. https:\/\/doi.org\/10.1016\/j.fertnstert.2018.05.006","journal-title":"Fertil Steril"},{"key":"2024_CR55","doi-asserted-by":"publisher","unstructured":"Grinsztajn L, Oyallon E, Varoquaux G (2022) Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems 35. Curran Associates, Red Hook NY 507\u2013520. https:\/\/doi.org\/10.52202\/068431-0037","DOI":"10.52202\/068431-0037"},{"key":"2024_CR56","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402\u20132410. https:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"JAMA"},{"key":"#cr-split#-2024_CR57.1","unstructured":"Guo C, Pleiss G, Sun Y, Weinberger KQ (2017) On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR"},{"key":"#cr-split#-2024_CR57.2","unstructured":"arXiv:1706.04599 70:1321-1330. https:\/\/arxiv.org\/abs\/1706.04599. Accessed 29 July 2026"},{"key":"2024_CR58","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.1038\/s41591-024-03097-1","volume":"30","author":"P Hager","year":"2024","unstructured":"Hager P, Jungmann F, Holland R, Bhagat K, Hubrecht I, Knauer M, Vielhauer J, Kirchhoff S, Kaissis G, Rueckert D, Braren R (2024) Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nat Med 30:2613\u20132622. https:\/\/doi.org\/10.1038\/s41591-024-03097-1","journal-title":"Nat Med"},{"key":"2024_CR59","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","volume":"184","author":"Y Hao","year":"2021","unstructured":"Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M et al (2021) Integrated analysis of multimodal single-cell data. Cell 184:3573\u20133587e29. https:\/\/doi.org\/10.1016\/j.cell.2021.04.048","journal-title":"Cell"},{"key":"2024_CR60","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","volume":"21","author":"M Hao","year":"2024","unstructured":"Hao M, Gong J, Zeng X, Liu C, Guo Y, Cheng X, Wang T, Ma J, Zhang X, Song L (2024) Large-scale foundation model on single-cell transcriptomics. Nat Methods 21:1481\u20131491. https:\/\/doi.org\/10.1038\/s41592-024-02305-7","journal-title":"Nat Methods"},{"key":"2024_CR61","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/clpt.2013.24","volume":"93","author":"R Harpaz","year":"2013","unstructured":"Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH (2013) Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin Pharmacol Ther 93:539\u2013546. https:\/\/doi.org\/10.1038\/clpt.2013.24","journal-title":"Clin Pharmacol Ther"},{"key":"2024_CR62","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.tips.2019.05.005","volume":"40","author":"S Harrer","year":"2019","unstructured":"Harrer S, Shah P, Antony B, Hu J (2019) Artificial intelligence for clinical trial design. Trends Pharmacol Sci 40:577\u2013591. https:\/\/doi.org\/10.1016\/j.tips.2019.05.005","journal-title":"Trends Pharmacol Sci"},{"key":"2024_CR63","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s13059-017-1215-1","volume":"18","author":"Y Hasin","year":"2017","unstructured":"Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18:83. https:\/\/doi.org\/10.1186\/s13059-017-1215-1","journal-title":"Genome Biol"},{"key":"2024_CR64","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41576-018-0071-5","volume":"20","author":"E Heitzer","year":"2019","unstructured":"Heitzer E, Haque IS, Roberts CES, Speicher MR (2019) Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet 20:71\u201388. https:\/\/doi.org\/10.1038\/s41576-018-0071-5","journal-title":"Nat Rev Genet"},{"key":"2024_CR65","doi-asserted-by":"publisher","first-page":"bbaa167","DOI":"10.1093\/bib\/bbaa167","volume":"22","author":"M Herrmann","year":"2021","unstructured":"Herrmann M, Probst P, Hornung R, Jurinovic V, Boulesteix AL (2021) Large-scale benchmark study of survival prediction methods using multi-omics data. Brief Bioinform 22:bbaa167. https:\/\/doi.org\/10.1093\/bib\/bbaa167","journal-title":"Brief Bioinform"},{"key":"2024_CR66","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.cell.2018.03.022","volume":"173","author":"KA Hoadley","year":"2018","unstructured":"Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, Shen R, Taylor AM, Cherniack AD, Thorsson V, Akbani R, Bowlby R, Wong CK, Wiznerowicz M, Sanchez-Vega F, Robertson AG, Schneider BG, Lawrence MS, Noushmehr H, Malta TM et al (2018) Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173:291\u2013304e6 [24 authors total]. https:\/\/doi.org\/10.1016\/j.cell.2018.03.022","journal-title":"Cell"},{"key":"2024_CR67","doi-asserted-by":"publisher","first-page":"2056","DOI":"10.1038\/s41588-023-01574-w","volume":"55","author":"C Huang","year":"2023","unstructured":"Huang C, Shuai RW, Baokar P, Chung R, Rastogi R, Kathail P, Ioannidis NM (2023) Personal transcriptome variation is poorly explained by current genomic deep learning models. Nat Genet 55:2056\u20132059. https:\/\/doi.org\/10.1038\/s41588-023-01574-w","journal-title":"Nat Genet"},{"key":"2024_CR68","doi-asserted-by":"publisher","first-page":"zcab007","DOI":"10.1093\/narcan\/zcab007","volume":"3","author":"S Ibing","year":"2021","unstructured":"Ibing S, Michels BE, Mosdzien M, Meyer HR, Feuerbach L, K\u00f6rner C (2021) On the impact of batch effect correction in TCGA isomiR expression data. NAR Cancer 3:zcab007. https:\/\/doi.org\/10.1093\/narcan\/zcab007","journal-title":"NAR Cancer"},{"key":"2024_CR69","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.cell.2018.12.015","volume":"176","author":"K Jaganathan","year":"2019","unstructured":"Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB, Chow ED, Kanterakis E, Gao H, Kia A, Batzoglou S, Sanders SJ, Farh KK (2019) Predicting splicing from primary sequence with deep learning. Cell 176:535\u2013548e24. https:\/\/doi.org\/10.1016\/j.cell.2018.12.015","journal-title":"Cell"},{"key":"2024_CR70","doi-asserted-by":"publisher","first-page":"5792","DOI":"10.1038\/s41598-021-84973-5","volume":"11","author":"Z Jie","year":"2021","unstructured":"Jie Z, Zhiying Z, Li L (2021) A meta-analysis of Watson for Oncology in clinical application. Sci Rep 11:5792. https:\/\/doi.org\/10.1038\/s41598-021-84973-5","journal-title":"Sci Rep"},{"key":"2024_CR71","unstructured":"Jin W, Barzilay R, Jaakkola T (2018) Junction tree variational autoencoder for molecular graph generation. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80:2323\u20132332. https:\/\/arxiv.org\/abs\/1802.04364. Accessed 29 July 2026"},{"key":"2024_CR72","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1093\/biostatistics\/kxj037","volume":"8","author":"WE Johnson","year":"2007","unstructured":"Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118\u2013127. https:\/\/doi.org\/10.1093\/biostatistics\/kxj037","journal-title":"Biostatistics"},{"key":"2024_CR73","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, \u017d\u00eddek A, Potapenko A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583\u2013589. https:\/\/doi.org\/10.1038\/s41586-021-03819-2","journal-title":"Nature"},{"key":"2024_CR74","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1177\/09622802251338983","volume":"34","author":"O Kalayc\u0131o\u011flu","year":"2025","unstructured":"Kalayc\u0131o\u011flu O, Pavlou M, Akhanl\u0131 SE, de Belder MA, Ambler G, Omar RZ (2025) Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction. Stat Methods Med Res 34:1356\u20131372. https:\/\/doi.org\/10.1177\/09622802251338983","journal-title":"Stat Methods Med Res"},{"key":"2024_CR75","doi-asserted-by":"publisher","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","volume":"4","author":"S Kapoor","year":"2023","unstructured":"Kapoor S, Narayanan A (2023) Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4:100804. https:\/\/doi.org\/10.1016\/j.patter.2023.100804","journal-title":"Patterns"},{"key":"2024_CR76","doi-asserted-by":"publisher","first-page":"111732","DOI":"10.1016\/j.jclinepi.2025.111732","volume":"181","author":"T Kaul","year":"2025","unstructured":"Kaul T, Damen JAA, Wynants L, Van Calster B, van Smeden M, Hooft L, Moons KGM (2025) Assessing the quality of prediction models in health care using the Prediction model Risk Of Bias ASsessment Tool (PROBAST): an evaluation of its use and practical application. J Clin Epidemiol 181:111732. https:\/\/doi.org\/10.1016\/j.jclinepi.2025.111732","journal-title":"J Clin Epidemiol"},{"key":"2024_CR77","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s13059-025-03574-x","volume":"26","author":"KZ Kedzierska","year":"2025","unstructured":"Kedzierska KZ, Crawford L, Amini AP, Lu AX (2025) Zero-shot evaluation reveals limitations of single-cell foundation models. Genome Biol 26:101. https:\/\/doi.org\/10.1186\/s13059-025-03574-x","journal-title":"Genome Biol"},{"key":"2024_CR78","doi-asserted-by":"publisher","unstructured":"Kendiukhov I (2026a) Systematic evaluation of single-cell foundation model interpretability: attention-derived edge scores add no incremental value over gene-level features for perturbation-target prediction. BMC Genomics 27. https:\/\/doi.org\/10.1186\/s12864-026-12965-8","DOI":"10.1186\/s12864-026-12965-8"},{"key":"2024_CR79","doi-asserted-by":"publisher","first-page":"105864","DOI":"10.1016\/j.biosystems.2026.105864","volume":"266","author":"I Kendiukhov","year":"2026","unstructured":"Kendiukhov I (2026b) Partial-label metric ceilings for evaluating gene regulatory networks inferred from single-cell foundation models. BioSystems 266:105864. https:\/\/doi.org\/10.1016\/j.biosystems.2026.105864","journal-title":"BioSystems"},{"key":"2024_CR91","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","volume":"21","author":"D L\u00e4hnemann","year":"2020","unstructured":"L\u00e4hnemann D, K\u00f6ster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A et al (2020) Eleven grand challenges in single-cell data science. Genome Biol 21:31. https:\/\/doi.org\/10.1186\/s13059-020-1926-6","journal-title":"Genome Biol"},{"key":"2024_CR92","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1186\/1471-2105-12-253","volume":"12","author":"KA L\u00ea Cao","year":"2011","unstructured":"L\u00ea Cao KA, Boitard S, Besse P (2011) Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253. https:\/\/doi.org\/10.1186\/1471-2105-12-253","journal-title":"BMC Bioinformatics"},{"key":"2024_CR80","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"2024_CR81","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1038\/nrg2825","volume":"11","author":"JT Leek","year":"2010","unstructured":"Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11:733\u2013739. https:\/\/doi.org\/10.1038\/nrg2825","journal-title":"Nat Rev Genet"},{"key":"2024_CR82","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1080\/01621459.2017.1307116","volume":"113","author":"J Lei","year":"2018","unstructured":"Lei J, G\u2019Sell M, Rinaldo A, Tibshirani RJ, Wasserman L (2018) Distribution-free predictive inference for regression. J Am Stat Assoc 113:1094\u20131111. https:\/\/doi.org\/10.1080\/01621459.2017.1307116","journal-title":"J Am Stat Assoc"},{"key":"2024_CR83","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1186\/s13059-022-02739-2","volume":"23","author":"D Leng","year":"2022","unstructured":"Leng D, Zheng L, Wen Y, Zhang Y, Wu L, Wang J, Wang M, Zhang Z, He S, Bo X (2022) A benchmark study of deep learning-based multi-omics data fusion methods for cancer. Genome Biol 23:171. https:\/\/doi.org\/10.1186\/s13059-022-02739-2","journal-title":"Genome Biol"},{"key":"2024_CR84","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1186\/s12911-024-02642-9","volume":"24","author":"Y Li","year":"2024","unstructured":"Li Y, Herold T, Mansmann U, Hornung R (2024) Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study. BMC Med Inf Decis Mak 24:244. https:\/\/doi.org\/10.1186\/s12911-024-02642-9","journal-title":"BMC Med Inf Decis Mak"},{"key":"2024_CR85","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y et al (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379:1123\u20131130. https:\/\/doi.org\/10.1126\/science.ade2574","journal-title":"Science"},{"key":"2024_CR86","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","volume":"26","author":"X Liu","year":"2020","unstructured":"Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26:1364\u20131374. https:\/\/doi.org\/10.1038\/s41591-020-1034-x","journal-title":"Nat Med"},{"key":"2024_CR87","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1038\/s41576-021-00370-8","volume":"22","author":"SK Longo","year":"2021","unstructured":"Longo SK, Guo MG, Ji AL, Khavari PA (2021) Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 22:627\u2013644. https:\/\/doi.org\/10.1038\/s41576-021-00370-8","journal-title":"Nat Rev Genet"},{"key":"2024_CR88","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/s41587-021-01001-7","volume":"40","author":"M Lotfollahi","year":"2022","unstructured":"Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, B\u00fcttner M, Wagenstetter M, Avsec \u017d, Gayoso A, Yosef N, Interlandi M et al (2022) Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol 40:121\u2013130. https:\/\/doi.org\/10.1038\/s41587-021-01001-7","journal-title":"Nat Biotechnol"},{"key":"2024_CR89","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","volume":"19","author":"MD Luecken","year":"2022","unstructured":"Luecken MD, B\u00fcttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colom\u00e9-Tatch\u00e9 M et al (2022) Benchmarking atlas-level data integration in single-cell genomics. Nat Methods 19:41\u201350. https:\/\/doi.org\/10.1038\/s41592-021-01336-8","journal-title":"Nat Methods"},{"key":"2024_CR90","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1038\/s41587-025-02694-w","volume":"43","author":"MD Luecken","year":"2025","unstructured":"Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, Strobl DC, Markov NS, Zappia L, Palla G, Lewis W, Dimitrov D, Vinyard ME, Magruder DS, Mueller MF, Andersson A, Dann E, Qin Q, Otto DJ, Klein M, Botvinnik OB, Deconinck L et al (2025) Defining and benchmarking open problems in single-cell analysis. Nat Biotechnol 43:1035\u20131040 [34 authors total]. https:\/\/doi.org\/10.1038\/s41587-025-02694-w","journal-title":"Nat Biotechnol"},{"key":"2024_CR93","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1038\/s41588-019-0379-x","volume":"51","author":"AR Martin","year":"2019","unstructured":"Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ (2019) Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51:584\u2013591. https:\/\/doi.org\/10.1038\/s41588-019-0379-x","journal-title":"Nat Genet"},{"key":"2024_CR94","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/s11306-019-1612-4","volume":"15","author":"KM Mendez","year":"2019","unstructured":"Mendez KM, Reinke SN, Broadhurst DI (2019) A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification. Metabolomics 15:150. https:\/\/doi.org\/10.1007\/s11306-019-1612-4","journal-title":"Metabolomics"},{"key":"2024_CR95","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/1471-2105-15-162","volume":"15","author":"C Meng","year":"2014","unstructured":"Meng C, Kuster B, Culhane AC, Gholami AM (2014) A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15:162. https:\/\/doi.org\/10.1186\/1471-2105-15-162","journal-title":"BMC Bioinformatics"},{"key":"2024_CR96","doi-asserted-by":"publisher","first-page":"3768","DOI":"10.3390\/cancers13153768","volume":"13","author":"V Modhukur","year":"2021","unstructured":"Modhukur V, Sharma S, Mondal M, Lawarde A, Kask K, Sharma R, Salumets A (2021) Machine learning approaches to classify primary and metastatic cancers using tissue of origin-based DNA methylation profiles. Cancers 13:3768. https:\/\/doi.org\/10.3390\/cancers13153768","journal-title":"Cancers"},{"key":"2024_CR97","doi-asserted-by":"publisher","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","volume":"388","author":"KGM Moons","year":"2025","unstructured":"Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, Beam AL, Van Calster B, Celi LA, Denaxas S et al (2025) PROBAST\u2009+\u2009AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ 388:e082505. https:\/\/doi.org\/10.1136\/bmj-2024-082505","journal-title":"BMJ"},{"key":"2024_CR98","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","volume":"616","author":"M Moor","year":"2023","unstructured":"Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P (2023) Foundation models for generalist medical artificial intelligence. Nature 616:259\u2013265. https:\/\/doi.org\/10.1038\/s41586-023-05881-4","journal-title":"Nature"},{"key":"2024_CR99","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1093\/biostatistics\/kxv027","volume":"17","author":"V Nygaard","year":"2016","unstructured":"Nygaard V, R\u00f8dland EA, Hovig E (2016) Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics 17:29\u201339. https:\/\/doi.org\/10.1093\/biostatistics\/kxv027","journal-title":"Biostatistics"},{"key":"2024_CR100","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer Z, Powers B, Vogeli C, Mullainathan S (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366:447\u2013453. https:\/\/doi.org\/10.1126\/science.aax2342","journal-title":"Science"},{"key":"2024_CR101","doi-asserted-by":"publisher","first-page":"1507448","DOI":"10.3389\/fbinf.2025.1507448","volume":"5","author":"C Papangelou","year":"2025","unstructured":"Papangelou C, Kyriakidis K, Natsiavas P, Chouvarda I, Malousi A (2025) Reliable machine learning models in genomic medicine using conformal prediction. Front Bioinform 5:1507448. https:\/\/doi.org\/10.3389\/fbinf.2025.1507448","journal-title":"Front Bioinform"},{"key":"2024_CR102","doi-asserted-by":"publisher","first-page":"7346","DOI":"10.1038\/s41467-022-33407-5","volume":"13","author":"S Pati","year":"2022","unstructured":"Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C et al (2022) Federated learning enables big data for rare cancer boundary detection. Nat Commun 13:7346. https:\/\/doi.org\/10.1038\/s41467-022-33407-5","journal-title":"Nat Commun"},{"key":"2024_CR103","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nrd3078","volume":"9","author":"SM Paul","year":"2010","unstructured":"Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry\u2019s grand challenge. Nat Rev Drug Discov 9:203\u2013214. https:\/\/doi.org\/10.1038\/nrd3078","journal-title":"Nat Rev Drug Discov"},{"key":"2024_CR104","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/s41746-020-0304-9","volume":"3","author":"JK Paulus","year":"2020","unstructured":"Paulus JK, Kent DM (2020) Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. npj Digit Med 3:99. https:\/\/doi.org\/10.1038\/s41746-020-0304-9","journal-title":"npj Digit Med"},{"key":"2024_CR105","volume-title":"The book of why: the new science of cause and effect","author":"J Pearl","year":"2018","unstructured":"Pearl J, Mackenzie D (2018) The book of why: the new science of cause and effect. Basic Books, New York"},{"key":"2024_CR106","doi-asserted-by":"publisher","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","volume":"19","author":"M Picard","year":"2021","unstructured":"Picard M, Scott-Boyer MP, Bodein A, P\u00e9rin O, Droit A (2021) Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J 19:3735\u20133746. https:\/\/doi.org\/10.1016\/j.csbj.2021.06.030","journal-title":"Comput Struct Biotechnol J"},{"key":"2024_CR107","unstructured":"Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers. MIT Press, Cambridge MA, pp 61\u201374. https:\/\/www.microsoft.com\/en-us\/research\/publication\/probabilistic-outputs-for-support-vector-machines-and-comparisons-to-regularized-likelihood-methods\/. Accessed 29 July 2026"},{"key":"2024_CR108","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nrd.2018.168","volume":"18","author":"S Pushpakom","year":"2019","unstructured":"Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, McNamee C et al (2019) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 18:41\u201358. https:\/\/doi.org\/10.1038\/nrd.2018.168","journal-title":"Nat Rev Drug Discov"},{"key":"2024_CR109","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41746-025-01459-8","volume":"8","author":"FE Rademakers","year":"2025","unstructured":"Rademakers FE, Biasin E, Bruining N, Caiani EG, Davies RH, Gilbert SH, Kamenjasevic E, McGauran G, O\u2019Connor G, Rouffet JB et al (2025) CORE-MD clinical risk score for regulatory evaluation of artificial intelligence-based medical device software. NPJ Digit Med 8:90. https:\/\/doi.org\/10.1038\/s41746-025-01459-8","journal-title":"NPJ Digit Med"},{"key":"2024_CR110","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380:1347\u20131358. https:\/\/doi.org\/10.1056\/NEJMra1814259","journal-title":"N Engl J Med"},{"key":"2024_CR111","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","volume":"14","author":"CL Ramspek","year":"2021","unstructured":"Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M (2021) External validation of prognostic models: what, why, how, when and where? Clin Kidney J 14:49\u201358. https:\/\/doi.org\/10.1093\/ckj\/sfaa188","journal-title":"Clin Kidney J"},{"key":"2024_CR112","doi-asserted-by":"publisher","first-page":"10546","DOI":"10.1093\/nar\/gky889","volume":"46","author":"N Rappoport","year":"2018","unstructured":"Rappoport N, Shamir R (2018) Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res 46:10546\u201310562. https:\/\/doi.org\/10.1093\/nar\/gky889","journal-title":"Nucleic Acids Res"},{"key":"2024_CR113","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1093\/bioinformatics\/btz058","volume":"35","author":"N Rappoport","year":"2019","unstructured":"Rappoport N, Shamir R (2019) NEMO: cancer subtyping by integration of partial multi-omic data. Bioinformatics 35:3348\u20133356. https:\/\/doi.org\/10.1093\/bioinformatics\/btz058","journal-title":"Bioinformatics"},{"key":"2024_CR114","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1186\/s12885-019-5994-5","volume":"19","author":"R Rasnic","year":"2019","unstructured":"Rasnic R, Brandes N, Zuk O, Linial M (2019) Substantial batch effects in TCGA exome sequences undermine pan-cancer analysis of germline variants. BMC Cancer 19:783. https:\/\/doi.org\/10.1186\/s12885-019-5994-5","journal-title":"BMC Cancer"},{"key":"2024_CR115","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke N, Hancox J, Li W, Milletar\u00ec F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K et al (2020) The future of digital health with federated learning. npj Digit Med 3:119. https:\/\/doi.org\/10.1038\/s41746-020-00323-1","journal-title":"npj Digit Med"},{"key":"2024_CR117","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1002\/sim.7993","volume":"38","author":"RD Riley","year":"2019","unstructured":"Riley RD, Snell KIE, Ensor J, Burke DL, Harrell FE Jr, Moons KGM, Collins GS (2019a) Minimum sample size for developing a multivariable prediction model: Part I - continuous outcomes. Stat Med 38:1262\u20131275. https:\/\/doi.org\/10.1002\/sim.7993","journal-title":"Stat Med"},{"key":"2024_CR116","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1002\/sim.7992","volume":"38","author":"RD Riley","year":"2019","unstructured":"Riley RD, Snell KIE, Ensor J, Burke DL, Harrell FE Jr, Moons KGM, Collins GS (2019b) Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med 38:1276\u20131296. https:\/\/doi.org\/10.1002\/sim.7992","journal-title":"Stat Med"},{"key":"2024_CR118","doi-asserted-by":"publisher","unstructured":"Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick CL, Ma J et al (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci USA 118. https:\/\/doi.org\/10.1073\/pnas.2016239118","DOI":"10.1073\/pnas.2016239118"},{"key":"2024_CR119","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/nature14248","volume":"518","author":"A Kundaje","year":"2015","unstructured":"Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317\u2013330. https:\/\/doi.org\/10.1038\/nature14248","journal-title":"Nature"},{"key":"2024_CR120","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","volume":"3","author":"M Roberts","year":"2021","unstructured":"Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L et al (2021) Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3:199\u2013217. https:\/\/doi.org\/10.1038\/s42256-021-00307-0","journal-title":"Nat Mach Intell"},{"key":"2024_CR121","doi-asserted-by":"publisher","first-page":"e235","DOI":"10.1016\/S2589-7500(22)00017-6","volume":"4","author":"P Ruamviboonsuk","year":"2022","unstructured":"Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, Raman R, Levinstein B, Liu Y, Schaekermann M et al (2022) Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health 4:e235\u2013e244. https:\/\/doi.org\/10.1016\/S2589-7500(22)00017-6","journal-title":"Lancet Digit Health"},{"key":"2024_CR122","doi-asserted-by":"publisher","unstructured":"Sallam M (2023) ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare 11. https:\/\/doi.org\/10.3390\/healthcare11060887","DOI":"10.3390\/healthcare11060887"},{"key":"2024_CR123","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/jproc.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek W, Montavon G, Lapuschkin S, Anders CJ, Muller KR (2021) Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE 109:247\u2013278. https:\/\/doi.org\/10.1109\/jproc.2021.3060483","journal-title":"Proc IEEE"},{"key":"2024_CR124","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/nrd.2016.230","volume":"16","author":"R Santos","year":"2017","unstructured":"Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS, Bologa CG, Karlsson A, Al-Lazikani B, Hersey A, Oprea TI et al (2017) A comprehensive map of molecular drug targets. Nat Rev Drug Discov 16:19\u201334. https:\/\/doi.org\/10.1038\/nrd.2016.230","journal-title":"Nat Rev Drug Discov"},{"key":"2024_CR125","doi-asserted-by":"publisher","first-page":"2060","DOI":"10.1038\/s41588-023-01524-6","volume":"55","author":"A Sasse","year":"2023","unstructured":"Sasse A, Ng B, Spiro AE, Tasaki S, Bennett DA, Gaiteri C, De Jager PL, Chikina M, Mostafavi S (2023) Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings. Nat Genet 55:2060\u20132064. https:\/\/doi.org\/10.1038\/s41588-023-01524-6","journal-title":"Nat Genet"},{"key":"2024_CR126","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/nrd3681","volume":"11","author":"JW Scannell","year":"2012","unstructured":"Scannell JW, Blanckley A, Boldon H, Warrington B (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11:191\u2013200. https:\/\/doi.org\/10.1038\/nrd3681","journal-title":"Nat Rev Drug Discov"},{"key":"2024_CR127","doi-asserted-by":"publisher","first-page":"29166","DOI":"10.52202\/079017-0919","volume":"37","author":"A Sharma","year":"2024","unstructured":"Sharma A, Keh S, Mitchell E, Finn C, Arora K, Kollar T (2024) A critical evaluation of AI feedback for aligning large language models. Adv Neural Inf Process Syst 37:29166\u201329190. https:\/\/doi.org\/10.52202\/079017-0919","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024_CR128","doi-asserted-by":"publisher","first-page":"12598","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, Milchenko M, Xu W, Marcus D, Colen RR et al (2020) Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 10:12598. https:\/\/doi.org\/10.1038\/s41598-020-69250-1","journal-title":"Sci Rep"},{"key":"2024_CR129","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1038\/nbt.1665","volume":"28","author":"L Shi","year":"2010","unstructured":"Shi L, Campbell G, Jones WD, Campagne F, Wen Z, Walker SJ, Su Z, Chu TM, Goodsaid FM, Pusztai L, Shaughnessy JD Jr, Oberthuer A, Thomas RS, Paules RS, Fielden M, Barlogie B, Chen W, Du P, Fischer M, Furlanello C et al (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol 28:827\u2013838 [203 authors total]. https:\/\/doi.org\/10.1038\/nbt.1665","journal-title":"Nat Biotechnol"},{"key":"2024_CR130","doi-asserted-by":"publisher","unstructured":"Shokri R, Stronati M, Song C, Shmatikov V (2017) Membership inference attacks against machine learning models. 2017 IEEE Symp Secur Priv (SP) 3\u201318. https:\/\/doi.org\/10.1109\/SP.2017.41","DOI":"10.1109\/SP.2017.41"},{"key":"2024_CR131","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv R, Armon A (2022) Tabular data: deep learning is not all you need. Inf Fusion 81:84\u201390. https:\/\/doi.org\/10.1016\/j.inffus.2021.11.011","journal-title":"Inf Fusion"},{"key":"2024_CR132","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1093\/bioinformatics\/bty1054","volume":"35","author":"A Singh","year":"2019","unstructured":"Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, L\u00ea Cao KA (2019) DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35:3055\u20133062. https:\/\/doi.org\/10.1093\/bioinformatics\/bty1054","journal-title":"Bioinformatics"},{"key":"2024_CR133","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S et al (2023) Large language models encode clinical knowledge. Nature 620:172\u2013180. https:\/\/doi.org\/10.1038\/s41586-023-06291-2","journal-title":"Nature"},{"key":"2024_CR134","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1056\/NEJMc2029240","volume":"383","author":"MW Sjoding","year":"2020","unstructured":"Sjoding MW, Dickson RP, Iwashyna TJ, Gay SE, Valley TS (2020) Racial bias in pulse oximetry measurement. N Engl J Med 383:2477\u20132478. https:\/\/doi.org\/10.1056\/NEJMc2029240","journal-title":"N Engl J Med"},{"key":"2024_CR135","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1038\/nprot.2015.124","volume":"10","author":"D Smedley","year":"2015","unstructured":"Smedley D, Jacobsen JO, J\u00e4ger M, K\u00f6hler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL et al (2015) Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat Protoc 10:2004\u20132015. https:\/\/doi.org\/10.1038\/nprot.2015.124","journal-title":"Nat Protoc"},{"key":"2024_CR136","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s12859-020-3427-8","volume":"21","author":"AM Smith","year":"2020","unstructured":"Smith AM, Walsh JR, Long J, Davis CB, Henstock P, Hodge MR, Maciejewski M, Mu XJ, Ra S, Zhao S, Ziemek D, Fisher CK (2020) Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data. BMC Bioinformatics 21:119. https:\/\/doi.org\/10.1186\/s12859-020-3427-8","journal-title":"BMC Bioinformatics"},{"key":"2024_CR137","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1093\/annonc\/mdx781","volume":"29","author":"SP Somashekhar","year":"2018","unstructured":"Somashekhar SP, Sep\u00falveda MJ, Puglielli S, Norden AD, Shortliffe EH, Rohit Kumar C, Rauthan A, Arun Kumar N, Patil P, Rhee K et al (2018) Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol 29:418\u2013423. https:\/\/doi.org\/10.1093\/annonc\/mdx781","journal-title":"Ann Oncol"},{"key":"2024_CR138","doi-asserted-by":"publisher","first-page":"117793221989905","DOI":"10.1177\/1177932219899051","volume":"14","author":"I Subramanian","year":"2020","unstructured":"Subramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 14:1177932219899051. https:\/\/doi.org\/10.1177\/1177932219899051","journal-title":"Bioinform Biol Insights"},{"key":"2024_CR139","first-page":"3319","volume":"PMLR 70","author":"M Sundararajan","year":"2017","unstructured":"Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. Proc 34th Int Conf Mach Learn (ICML) PMLR 70:3319\u20133328. https:\/\/arxiv.org\/abs\/1703.01365 Accessed 29 July 2026","journal-title":"Proc 34th Int Conf Mach Learn (ICML)"},{"key":"2024_CR140","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","volume":"618","author":"CV Theodoris","year":"2023","unstructured":"Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, Hill MC, Mantineo H, Brydon EM, Zeng Z, Liu XS et al (2023) Transfer learning enables predictions in network biology. Nature 618:616\u2013624. https:\/\/doi.org\/10.1038\/s41586-023-06139-9","journal-title":"Nature"},{"key":"2024_CR141","doi-asserted-by":"publisher","first-page":"100911","DOI":"10.1016\/j.landig.2025.100911","volume":"7","author":"D Thomassen","year":"2025","unstructured":"Thomassen D, Hackmann T, Goeman J, Steyerberg E, le Cessie S (2025) Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models. Lancet Digit Health 7:100911. https:\/\/doi.org\/10.1016\/j.landig.2025.100911","journal-title":"Lancet Digit Health"},{"key":"2024_CR142","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Methodol 58:267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2024_CR144","doi-asserted-by":"publisher","first-page":"e00434","DOI":"10.1128\/mBio.00434-20","volume":"11","author":"BD Top\u00e7uo\u011flu","year":"2020","unstructured":"Top\u00e7uo\u011flu BD, Lesniak NA, Ruffin MT 4th, Wiens J, Schloss PD (2020) A framework for effective application of machine learning to microbiome-based classification problems. mBio 11:e00434\u2013e00420. https:\/\/doi.org\/10.1128\/mBio.00434-20","journal-title":"mBio"},{"key":"2024_CR143","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"key":"2024_CR145","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","volume":"21","author":"HTN Tran","year":"2020","unstructured":"Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J (2020) A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21:12. https:\/\/doi.org\/10.1186\/s13059-019-1850-9","journal-title":"Genome Biol"},{"key":"2024_CR146","volume-title":"Marketing submission recommendations for a predetermined change control plan for artificial intelligence-enabled device software functions: guidance for industry and food and drug administration staff","author":"US Food and Drug Administration","year":"2024","unstructured":"US Food and Drug Administration (2024) Marketing submission recommendations for a predetermined change control plan for artificial intelligence-enabled device software functions: guidance for industry and food and drug administration staff. US Food and Drug Administration, Silver Spring MD. https:\/\/www.federalregister.gov\/d\/2024-28361 Docket FDA-2022-D-2628. Accessed 29 July 2026"},{"key":"2024_CR147","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","volume":"18","author":"J Vamathevan","year":"2019","unstructured":"Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M et al (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463\u2013477. https:\/\/doi.org\/10.1038\/s41573-019-0024-5","journal-title":"Nat Rev Drug Discov"},{"key":"2024_CR148","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.jclinepi.2015.12.005","volume":"74","author":"B Van Calster","year":"2016","unstructured":"Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW (2016) A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol 74:167\u2013176. https:\/\/doi.org\/10.1016\/j.jclinepi.2015.12.005","journal-title":"J Clin Epidemiol"},{"key":"2024_CR149","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1186\/s12916-019-1466-7","volume":"17","author":"B Van Calster","year":"2019","unstructured":"Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW (2019) Calibration: the Achilles heel of predictive analytics. BMC Med 17:230. https:\/\/doi.org\/10.1186\/s12916-019-1466-7","journal-title":"BMC Med"},{"key":"2024_CR3","doi-asserted-by":"publisher","first-page":"6070","DOI":"10.1038\/s41380-025-03336-y","volume":"30","author":"LA van de Mortel","year":"2025","unstructured":"van de Mortel LA, van Wingen GA (2025) Data leakage in machine learning studies creep into meta-analytic estimates of predictive performance. Mol Psychiatry 30:6070\u20136071. https:\/\/doi.org\/10.1038\/s41380-025-03336-y","journal-title":"Mol Psychiatry"},{"key":"2024_CR150","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","volume":"50","author":"M Varadi","year":"2022","unstructured":"Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A et al (2022) AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439\u2013D444. https:\/\/doi.org\/10.1093\/nar\/gkab1061","journal-title":"Nucleic Acids Res"},{"key":"2024_CR151","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-7-91","volume":"7","author":"S Varma","year":"2006","unstructured":"Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7:91. https:\/\/doi.org\/10.1186\/1471-2105-7-91","journal-title":"BMC Bioinformatics"},{"key":"2024_CR152","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","volume":"28","author":"B Vasey","year":"2022","unstructured":"Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M et al (2022) Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 28:924\u2013933. https:\/\/doi.org\/10.1038\/s41591-022-01772-9","journal-title":"Nat Med"},{"key":"2024_CR153","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s41666-021-00113-8","volume":"6","author":"J Vazquez","year":"2022","unstructured":"Vazquez J, Facelli JC (2022) Conformal prediction in clinical medical sciences. J Healthc Inf Res 6:241\u2013252. https:\/\/doi.org\/10.1007\/s41666-021-00113-8","journal-title":"J Healthc Inf Res"},{"key":"2024_CR154","doi-asserted-by":"publisher","first-page":"841","DOI":"10.2139\/ssrn.3063289","volume":"31","author":"S Wachter","year":"2018","unstructured":"Wachter S, Mittelstadt B, Russell C (2018) Counterfactual explanations without opening the black box: automated decisions and the GDPR. Harv J Law Technol 31:841\u2013887. https:\/\/doi.org\/10.2139\/ssrn.3063289","journal-title":"Harv J Law Technol"},{"key":"2024_CR155","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1038\/nrc.2017.7","volume":"17","author":"JCM Wan","year":"2017","unstructured":"Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, Pacey S, Baird R, Rosenfeld N (2017) Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 17:223\u2013238. https:\/\/doi.org\/10.1038\/nrc.2017.7","journal-title":"Nat Rev Cancer"},{"key":"2024_CR156","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nmeth.2810","volume":"11","author":"B Wang","year":"2014","unstructured":"Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, Haibe-Kains B, Goldenberg A (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11:333\u2013337. https:\/\/doi.org\/10.1038\/nmeth.2810","journal-title":"Nat Methods"},{"key":"2024_CR157","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1038\/s41467-021-23774-w","volume":"12","author":"T Wang","year":"2021","unstructured":"Wang T, Shao W, Huang Z, Tang H, Zhang J, Ding Z, Huang K (2021) MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun 12:3445. https:\/\/doi.org\/10.1038\/s41467-021-23774-w","journal-title":"Nat Commun"},{"key":"2024_CR158","doi-asserted-by":"publisher","unstructured":"Wang Y, Fan Y, Wang X, Yu T, Zong Y, Liu X, Zhong G, Liu M, Li Q, Lee KH et al (2026) SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration. Nat Commun 17. https:\/\/doi.org\/10.1038\/s41467-026-72570-x","DOI":"10.1038\/s41467-026-72570-x"},{"key":"2024_CR159","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-021-03583-3","volume":"594","author":"S Warnat-Herresthal","year":"2021","unstructured":"Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Handler K, Pickkers P, Aziz NA et al (2021) Swarm Learning for decentralized and confidential clinical machine learning. Nature 594:265\u2013270. https:\/\/doi.org\/10.1038\/s41586-021-03583-3","journal-title":"Nature"},{"key":"2024_CR160","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1001\/jama.2024.21451","volume":"333","author":"HJ Warraich","year":"2025","unstructured":"Warraich HJ, Tazbaz T, Califf RM (2025) FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine. JAMA 333:241\u2013247. https:\/\/doi.org\/10.1001\/jama.2024.21451","journal-title":"JAMA"},{"key":"2024_CR161","doi-asserted-by":"publisher","first-page":"bbaf166","DOI":"10.1093\/bib\/bbaf166","volume":"26","author":"G Wen","year":"2025","unstructured":"Wen G, Li L (2025) Federated transfer learning with differential privacy for multi-omics survival analysis. Brief Bioinform 26:bbaf166. https:\/\/doi.org\/10.1093\/bib\/bbaf166","journal-title":"Brief Bioinform"},{"key":"2024_CR162","doi-asserted-by":"publisher","first-page":"160018","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018. https:\/\/doi.org\/10.1038\/sdata.2016.18","journal-title":"Sci Data"},{"key":"2024_CR163","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074\u2013D1082. https:\/\/doi.org\/10.1093\/nar\/gkx1037","journal-title":"Nucleic Acids Res"},{"key":"2024_CR164","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170:51\u201358. https:\/\/doi.org\/10.7326\/M18-1376","journal-title":"Ann Intern Med"},{"key":"2024_CR165","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","volume":"181","author":"A Wong","year":"2021","unstructured":"Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, DeTroyer-Cooley O, Pestrue J, Phillips M, Konye J, Penoza C et al (2021) External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med 181:1065\u20131070. https:\/\/doi.org\/10.1001\/jamainternmed.2021.2626","journal-title":"JAMA Intern Med"},{"key":"2024_CR167","unstructured":"World Health Organization (2024) Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. World Health Organization, Geneva. https:\/\/iris.who.int\/handle\/10665\/375579. Accessed 29 July 2026"},{"key":"2024_CR166","unstructured":"World Health Organization (2021) Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization, Geneva. https:\/\/iris.who.int\/handle\/10665\/341996. Accessed 29 July 2026"},{"key":"2024_CR168","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","volume":"369","author":"L Wynants","year":"2020","unstructured":"Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA et al (2020) Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ 369:m1328. https:\/\/doi.org\/10.1136\/bmj.m1328","journal-title":"BMJ"},{"key":"2024_CR169","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1038\/s41591-024-02850-w","volume":"30","author":"F Yu","year":"2024","unstructured":"Yu F, Moehring A, Banerjee O, Salz T, Agarwal N, Rajpurkar P (2024) Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med 30:837\u2013849. https:\/\/doi.org\/10.1038\/s41591-024-02850-w","journal-title":"Nat Med"},{"key":"2024_CR170","doi-asserted-by":"publisher","first-page":"6567","DOI":"10.1038\/s41598-025-89733-3","volume":"15","author":"Z Yuan","year":"2025","unstructured":"Yuan Z, Lv G, Liu X, Xiao Y, Tan Y, Zhu Y (2025) Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis. Sci Rep 15:6567. https:\/\/doi.org\/10.1038\/s41598-025-89733-3","journal-title":"Sci Rep"},{"key":"2024_CR171","doi-asserted-by":"publisher","unstructured":"Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. Proc Eighth ACM SIGKDD Int Conf Knowl Discovery Data Min 694\u2013699. https:\/\/doi.org\/10.1145\/775047.775151","DOI":"10.1145\/775047.775151"},{"key":"2024_CR172","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med 15:e1002683. https:\/\/doi.org\/10.1371\/journal.pmed.1002683","journal-title":"PLoS Med"},{"key":"2024_CR173","doi-asserted-by":"publisher","first-page":"lqaa078","DOI":"10.1093\/nargab\/lqaa078","volume":"2","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Parmigiani G, Johnson WE (2020) ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform 2:lqaa078. https:\/\/doi.org\/10.1093\/nargab\/lqaa078","journal-title":"NAR Genom Bioinform"},{"key":"2024_CR174","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1093\/jjco\/hyaa051","volume":"50","author":"X Zhao","year":"2020","unstructured":"Zhao X, Zhang Y, Ma X, Chen Y, Xi J, Yin X, Kang H, Guan H, Dai Z, Liu D, Zhao F, Sun C, Li Z, Zhang S (2020) Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China. Jpn J Clin Oncol 50:852\u2013858. https:\/\/doi.org\/10.1093\/jjco\/hyaa051","journal-title":"Jpn J Clin Oncol"},{"key":"2024_CR175","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1002\/cpt.1795","volume":"107","author":"A Zhavoronkov","year":"2020","unstructured":"Zhavoronkov A, Vanhaelen Q, Oprea TI (2020) Will artificial intelligence for drug discovery impact clinical pharmacology? Clin Pharmacol Ther 107:780\u2013785. https:\/\/doi.org\/10.1002\/cpt.1795","journal-title":"Clin Pharmacol Ther"},{"key":"2024_CR176","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods 12:931\u2013934. https:\/\/doi.org\/10.1038\/nmeth.3547","journal-title":"Nat Methods"},{"key":"2024_CR177","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-030-63076-8_2","volume-title":"Federated learning: privacy and incentive. Lecture Notes in Computer Science","author":"L Zhu","year":"2020","unstructured":"Zhu L, Han S (2020) Deep leakage from gradients. In: Yang Q, Fan L, Yu H (eds) Federated learning: privacy and incentive. Lecture Notes in Computer Science, vol 12500. Springer, Cham, pp 17\u201331. https:\/\/doi.org\/10.1007\/978-3-030-63076-8_2"},{"key":"2024_CR178","doi-asserted-by":"publisher","first-page":"i457","DOI":"10.1093\/bioinformatics\/bty294","volume":"34","author":"M Zitnik","year":"2018","unstructured":"Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34:i457\u2013i466. https:\/\/doi.org\/10.1093\/bioinformatics\/bty294","journal-title":"Bioinformatics"},{"key":"2024_CR179","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67:301\u2013320. https:\/\/doi.org\/10.1111\/j.1467-9868.2005.00503.x","journal-title":"J R Stat Soc Ser B Stat Methodol"}],"container-title":["Functional &amp; Integrative Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10142-026-02024-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10142-026-02024-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10142-026-02024-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T02:42:40Z","timestamp":1788144160000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10142-026-02024-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":178,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2024"],"URL":"https:\/\/doi.org\/10.1007\/s10142-026-02024-6","relation":{},"ISSN":["1438-793X","1438-7948"],"issn-type":[{"value":"1438-793X","type":"print"},{"value":"1438-7948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"assertion":[{"value":"24 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":3,"name":"Ethics","label":"Clinical trial number","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"An AI-assisted workflow was employed to support intermediate manuscript review using Claude Opus 5.0, developed by Anthropic and released in 2026, in Claude Science. Specific tasks included reviewing R code and figures for errors, cross-referencing tables and citations, and English language editing grounded on source materials. The authors reviewed, verified, and modified all AI-generated outputs, retaining full responsibility for the accuracy and integrity of the final manuscript.","order":4,"name":"Ethics","label":"Use of artificial intelligence","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"238"}}