Data science and molecular biology: prediction and mechanistic explanation.
In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First,...
| Published in: | Synthese Vol. 198; no. 4; pp. 3131 - 3157 |
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| Main Authors: | , |
| Format: | Article |
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Springer Nature
Apr2021
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=149905566&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 149905566 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2021 vid: 198 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 149905566 10.1007/s11229-019-02271-0 ppf: 3131 ppct: 26 formats: fmt: @attributes: type: P size: 381KB tig: atl: Data science and molecular biology: prediction and mechanistic explanation. aug: au: López-Rubio, Ezequiel Ratti, Emanuele affil: Departamento de Lógica, Historia y Filosofía de la Ciencia, Universidad Nacional de Educación a Distancia (UNED), Paseo de Senda del Rey 7, 28040, Madrid, Spain Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga (UMA), Bulevar Louis Pasteur 35, 29071, Málaga, Spain Reilly Center for Science, Technology, and Values, Department of Philosophy, University of Notre Dame, Notre Dame, USA su: Data science Molecular biology Machine learning Prediction models Molecular models Biologists sug: subj: Data science Molecular biology Machine learning Prediction models Molecular models Biologists keyword: Biology Explanation Prediction ab: In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the 'new' biology is substantially diminished if compared to the 'old' biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2021. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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