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dc.rights.licenseopenen_US
hal.structure.identifierImmunology from Concept and Experiments to Translation = Immunologie Conceptuelle, Expérimentale et Translationnelle [ImmunoConcept]
dc.contributor.authorGROSS, Fridolin
dc.date.accessioned2024-10-17T14:59:36Z
dc.date.available2024-10-17T14:59:36Z
dc.date.issued2024-01
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/202563
dc.description.abstractEnThe philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs.
dc.language.isoENen_US
dc.title.enThe Explanatory Role of Machine Learning in Molecular Biology
dc.typeArticle de revueen_US
dc.identifier.doi10.1007/s10670-023-00772-6en_US
dc.subject.halSciences du Vivant [q-bio]/Immunologieen_US
bordeaux.journalErkenntnisen_US
bordeaux.hal.laboratoriesImmunoConcEpT - UMR 5164en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04742123
hal.version1
hal.date.transferred2024-10-17T14:59:37Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Erkenntnis&rft.date=2024-01&rft.au=GROSS,%20Fridolin&rft.genre=article


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