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Obtaining genetics insights from deep learning via explainable artificial intelligence.

Title:	Obtaining genetics insights from deep learning via explainable artificial intelligence.
Authors:	Novakovsky G; Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.; Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada.; Dexter N; Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.; School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.; Libbrecht MW; School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada. maxwl@sfu.ca.; Wasserman WW; Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada. wyeth@cmmt.ubc.ca.; Mostafavi S; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA. saramos@cs.washington.edu.; Canadian Institute for Advanced Research, Toronto, Ontario, Canada. saramos@cs.washington.edu.
Source:	Nature reviews. Genetics [Nat Rev Genet] 2023 Feb; Vol. 24 (2), pp. 125-137. Date of Electronic Publication: 2022 Oct 03.
Publication Type:	Journal Article; Review; Research Support, Non-U.S. Gov't
Language:	English
Journal Info:	Publisher: Nature Pub. Group Country of Publication: England NLM ID: 100962779 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-0064 (Electronic) Linking ISSN: 14710056 NLM ISO Abbreviation: Nat Rev Genet Subsets: MEDLINE
Imprint Name(s):	Original Publication: London, UK : Nature Pub. Group, [2000-
MeSH Terms:	Artificial Intelligence* ; Deep Learning*; Genomics
Abstract:	Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.; (© 2022. Springer Nature Limited.)
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Entry Date(s):	Date Created: 20221003 Date Completed: 20230123 Latest Revision: 20230306
Update Code:	20260130
DOI:	10.1038/s41576-022-00532-2
PMID:	36192604
Database:	MEDLINE

Journal Article; Review; Research Support, Non-U.S. Gov't