The Explanatory Role of Machine Learning in Molecular Biology: The Explanatory Role of Machine Learning...: F. Gross.
The 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 th...
| Publicado en: | Erkenntnis Vol. 90; no. 4; pp. 1583 - 1604 |
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| Formato: | Artículo |
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Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=184383507&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 184383507 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Apr2025 vid: 90 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 184383507 10.1007/s10670-023-00772-6 ppf: 1583 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 945KB tig: atl: The Explanatory Role of Machine Learning in Molecular Biology: The Explanatory Role of Machine Learning...: F. Gross. aug: au: Gross, Fridolin affil: https://ror.org/057qpr032 CNRS UMR5164 ImmunoConcEpT, Université de Bordeaux, Site de Carreire, Bâtiment BBS 146 Rue Léo Saignat, 33076, Bordeaux Cedex, France su: Molecular biology Machine learning Genetic regulation Biologists Artificial intelligence sug: subj: Molecular biology Machine learning Genetic regulation Biologists Artificial intelligence ab: The 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2025. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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