[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117016-en":3,"doc-seo-117016-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117016,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","The Explanatory Role of Machine Learning in Molecular Biology","The philosophical debate on machine learning in science often treats AI and classical methods as mutually exclusive choices, raising epistemological worries about opaque models that predict without explaining. Focusing on molecular biology, the work argues that machine learning is frequently deployed with explanatory goals and can be tightly integrated with traditional mechanistic research. Using a microRNA gene-regulation case study, it shows how machine learning can support causal insight and can function as a renewed beginning for theorizing rather than the end of theory.","The Explanatory Role of Machine Learning in Molecular Biology  \nFridolin Gross  \nUniversity of Bordeaux  \nforthcoming in Erkenntnis  \nAbstract  \nThe philosophical debate around the impact of machine learning in science is often framedin 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.  \n1 Introduction  \nArtificial intelligence (AI) methods based on machine learning techniques have recently led to some spectacular advances in many different fields, such as image recognition, language translation, or games like chess and Go. In the scientific context, the most recent example is the success of the AI system “AlphaFold” in predicting the folded structure of proteins based on their aminoacid sequence (Callaway, 2020; Jumper et al., 2021) . Machine learning gives rise to a number of epistemological issues due to the differences from classical computational methods. A common question is whether the widespread application of machine learning methods will limit scientific activity to a merely predictive enterprise that does not provide any insight into the causal processes underlying the investigated phenomena. Already more than a decade ago, Chris Anderson made the provocative claim in an article in WIRED that big data will herald the “end of theory”and make the traditional scientific method obsolete (Anderson, 2008) . The topic does not seem to have lost its appeal, as Laura Spinney recently discussed in The Guardian the question of whether we are “witnessing the dawn of post-theory science”(Spinney, 2022) . Philosophers of science and scientists have taken up the central question of this debate by addressing the impact that the introduction of AI and machine learning is likely to have on the general character of scientific research (e.g. Pietsch, 2015; Canali, 2016; Coveney et al., 2016; Boon, 2020; Creel, 2020; Ourmazd, 2020; Boge and Poznic, 2021; López-Rubio and Ratti, 2021; Boge et al., 2022; Krenn et al., 2022; Duede, 2023; Andrews, 2023) . The areas of genetics and molecular biology, which over the last few decades have become highly “data-centric”(Leonelli, 2016), seem particular prone to making  \nthe shift from a theory-or hypothesis-driven mode towards purely data-driven modes of research. While several philosophers have highlighted that the idea of a choice between hypothesis-driven and data-driven science is based on a false dichotomy, and that the more important question is how these modes of research can be integrated in scientific practice (e.g. O’Malley and Soyer, 2012), the discussion around machine learning seems to be commonly framed in terms of AI and classical methods as mutually exclusive alternatives.  \nBased on a case study from the field of microRNA research (McGeary et al., 2019), I show in this paper how in practice machine learning is often tightly integrated with mechanistic and explanatory research strategies. Far from precluding mechanistic insight, machine learning can actually contribute to the understanding of the causal factors underlying complex phenomena. My case study suggests that machine learning may in some scientific contexts not represen","cbCailKDBPA76FvO","https://ap.wps.com/l/cbCailKDBPA76FvO","pdf",390577,1,20,"English","en",105,"# Abstract\n# Introduction\n## AI, prediction, and the “end of theory” debate\n## Aim: integrating machine learning with mechanistic explanation\n# Case study: microRNA repression model\n## Section 2: basics of machine learning methods\n## Section 3: McGeary et al. model and research integration\n## Section 4: three claims about coupling and explanatory power\n# Conclusion","[{\"question\":\"What main concern does the philosophical debate raise about machine learning in science?\",\"answer\":\"It often worries that machine learning yields opaque models that can predict but cannot provide explanation or understanding of causal processes.\"},{\"question\":\"How does the document argue machine learning is actually used in molecular biology?\",\"answer\":\"It argues that machine learning is often employed with explanatory aims and can be tightly integrated with more traditional, mechanistic research methods.\"},{\"question\":\"What does the microRNA gene-regulation case study show?\",\"answer\":\"The case study suggests machine learning can contribute to insight into causal processes and, in some contexts, represents a new beginning for theorizing rather than an end of 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main concern does the philosophical debate raise about machine learning in science?","Question",{"text":74,"@type":75},"It often worries that machine learning yields opaque models that can predict but cannot provide explanation or understanding of causal processes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the document argue machine learning is actually used in molecular biology?",{"text":79,"@type":75},"It argues that machine learning is often employed with explanatory aims and can be tightly integrated with more traditional, mechanistic research methods.",{"name":81,"@type":72,"acceptedAnswer":82},"What does the microRNA gene-regulation case study show?",{"text":83,"@type":75},"The case study suggests machine learning can contribute to insight into causal processes and, in some contexts, represents a new beginning for theorizing rather than an end of 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