Interpretable machine learning methods for predictions in systems biology from omics data

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dc.contributor.authorSidak, Davidcs
dc.contributor.authorSchwarzerová, Janacs
dc.contributor.authorWeckwerth, Wolframcs
dc.contributor.authorWaldherr, Steffencs
dc.coverage.issueOctober 2022cs
dc.coverage.volume9cs
dc.date.issued2022-10-17cs
dc.description.abstractMachine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.en
dc.formattextcs
dc.format.extent1-28cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationFrontiers in Molecular Biosciences. 2022, vol. 9, issue October 2022, p. 1-28.en
dc.identifier.doi10.3389/fmolb.2022.926623cs
dc.identifier.issn2296-889Xcs
dc.identifier.orcid0000-0003-2918-9313cs
dc.identifier.other180012cs
dc.identifier.urihttp://hdl.handle.net/11012/208577
dc.language.isoencs
dc.publisherFrontierscs
dc.relation.ispartofFrontiers in Molecular Biosciencescs
dc.relation.urihttps://www.frontiersin.org/articles/10.3389/fmolb.2022.926623/fullcs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2296-889X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectmulti-omicsen
dc.subjectinterpretable machine learningen
dc.subjectdeep learningen
dc.subjectexplainable artificial intelligenceen
dc.subjectmetabolomicsen
dc.subjectproteomicsen
dc.subjecttranscriptomicsen
dc.titleInterpretable machine learning methods for predictions in systems biology from omics dataen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-180012en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:55en
sync.item.modts2025.01.17 16:47:27en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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