{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:00:37Z","timestamp":1790233237009,"version":"4.1.0"},"reference-count":36,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is rapidly transforming computational biology. However, closer scrutiny reveals that many reported results do not hold under rigorous evaluation. This paper identifies prevalent, recurring issues in the field, including narrow datasets, hidden confounders that make supposedly independent test data effectively indistinguishable from the training data (the doppelg\u00e4nger effect), misleading performance metrics, and over\u2010reliance on overly easy test cases. We argue that meaningful assessment must go beyond metric\u2010centric model evaluation to encompass biological realism, data diversity, confounding control, and interpretability. Crucially, AI studies in computational biology should deliver not only predictive performance but also genuine biological insight and transferable methodological understanding. To address these systemic limitations, we propose a unifying framework, VALID, comprising the following five guiding principles for rigorous AI in biology: Veracious biological grounding and novelty, artifact control and methodological rigor, legitimate generalization, inspectable preprocessing, and deployment\u2010aware performance. Together, these principles serve as a diagnostic lens for structuring our discussion throughout this paper. We further illustrate common pitfalls through representative examples and provide actionable guidance for authors, reviewers, and editors aimed at improving the rigor and scientific value of AI\u2010driven computational biology.<\/jats:p>","DOI":"10.1002\/qub2.70058","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:15:42Z","timestamp":1790230542000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The evaluation crisis in artificial intelligence\u2010driven computational biology: Pitfalls and solutions"],"prefix":"10.1002","volume":"14","author":[{"given":"Tsung Fei","family":"Khang","sequence":"first","affiliation":[{"name":"Institute of Mathematical Sciences, Faculty of Science Universiti Malaya  Kuala Lumpur Malaysia"},{"name":"Universiti Malaya Centre for Data Analytics Universiti Malaya  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua W.\u00a0K.","family":"Ho","sequence":"additional","affiliation":[{"name":"School of Biomedical Sciences, Li Ka Shing Faculty of Medicine The University of Hong Kong  Hong Kong China"},{"name":"School of Biomedical Engineering The University of Hong Kong  Hong Kong China"},{"name":"Centre for Health AI Research and Translation (CHART), Li Ka Shing Faculty of Medicine The University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Tsinghua University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology Monash University  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limsoon","family":"Wong","sequence":"additional","affiliation":[{"name":"School of Computing National University of Singapore  Republic of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002240"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2018.08.002"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.20866"},{"key":"e_1_2_15_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2020.100129"},{"key":"e_1_2_15_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-019-00857-9"},{"key":"e_1_2_15_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00146-021-01251-8"},{"key":"e_1_2_15_8_1","first-page":"4768","article-title":"A unified approach to interpreting model\u00a0predictions","volume":"30","author":"Lundberg SM","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_15_9_1","first-page":"1135","volume-title":"Proceedings of 22nd ACM SIGKDD international conference on knowledge discovery and data mining","author":"Ribeiro MT","year":"2016"},{"key":"e_1_2_15_10_1","first-page":"618","article-title":"Grad\u2010CAM: visual explanations from deep networks via gradient\u2010based localization","author":"Selvaraju RR","year":"2017","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"e_1_2_15_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02359-7"},{"key":"e_1_2_15_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_2_15_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5"},{"key":"e_1_2_15_14_1","doi-asserted-by":"crossref","unstructured":"OnerMU ChengYC LeeHK SungWK.Training machine learning models on patient level data segregation is crucial in practical clinical applications.2020. Preprint at medRxiv: 2020.04.23.20076406.","DOI":"10.1101\/2020.04.23.20076406"},{"key":"e_1_2_15_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804"},{"key":"e_1_2_15_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2021.10.017"},{"key":"e_1_2_15_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.09.007"},{"key":"e_1_2_15_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2025.13685"},{"key":"e_1_2_15_19_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012402"},{"key":"e_1_2_15_20_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219720025010012"},{"key":"e_1_2_15_21_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0042769"},{"key":"e_1_2_15_22_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295X.102.4.684"},{"key":"e_1_2_15_23_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.185.4157.1124"},{"key":"e_1_2_15_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty275"},{"key":"e_1_2_15_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-022-04626-w"},{"key":"e_1_2_15_26_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1951.tb00088.x"},{"key":"e_1_2_15_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3192"},{"key":"e_1_2_15_28_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2021-150089"},{"key":"e_1_2_15_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2023.103661"},{"key":"e_1_2_15_30_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.1668"},{"key":"e_1_2_15_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae128"},{"key":"e_1_2_15_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05946-3"},{"key":"e_1_2_15_33_1","unstructured":"SalehiM MirzaeiH HendrycksD LiY RohbanMH SabokrouM.A unified survey on anomaly novelty open\u2010set and out\u2010of\u2010distribution detection: solutions and future challenges.2021. Preprint at arXiv: 2110.14051."},{"key":"e_1_2_15_34_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjinnov-2019-000359"},{"key":"e_1_2_15_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00592-y"},{"key":"e_1_2_15_36_1","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adk3452"},{"issue":"7","key":"e_1_2_15_37_1","first-page":"10","article-title":"Cargo cult science","volume":"37","author":"Feynman RP","year":"1974","journal-title":"Eng Sci"}],"container-title":["Quantitative Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/qub2.70058","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/qub2.70058","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/qub2.70058","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:15:45Z","timestamp":1790230545000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/qub2.70058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["10.1002\/qub2.70058"],"URL":"https:\/\/doi.org\/10.1002\/qub2.70058","archive":["Portico"],"relation":{},"ISSN":["2095-4689","2095-4697"],"issn-type":[{"value":"2095-4689","type":"print"},{"value":"2095-4697","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]},"assertion":[{"value":"2025-10-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70058"}}