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SIDAK, D. SCHWARZEROVÁ, J. WECKWERTH, W. WALDHERR, S.
Originální název
Interpretable machine learning methods for predictions in systems biology from omics data
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
Machine 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.
Klíčová slova
multi-omics, interpretable machine learning, deep learning, explainable artificial intelligence, metabolomics, proteomics, transcriptomics
Autoři
SIDAK, D.; SCHWARZEROVÁ, J.; WECKWERTH, W.; WALDHERR, S.
Vydáno
17. 10. 2022
Nakladatel
Frontiers
ISSN
2296-889X
Periodikum
Frontiers in Molecular Biosciences
Ročník
9
Číslo
October 2022
Stát
Švýcarská konfederace
Strany od
1
Strany do
28
Strany počet
URL
https://www.frontiersin.org/articles/10.3389/fmolb.2022.926623/full
Plný text v Digitální knihovně
http://hdl.handle.net/11012/208577
BibTex
@article{BUT180012, author="David {Sidak} and Jana {Schwarzerová} and Wolfram {Weckwerth} and Steffen {Waldherr}", title="Interpretable machine learning methods for predictions in systems biology from omics data", journal="Frontiers in Molecular Biosciences", year="2022", volume="9", number="October 2022", pages="1--28", doi="10.3389/fmolb.2022.926623", issn="2296-889X", url="https://www.frontiersin.org/articles/10.3389/fmolb.2022.926623/full" }