Titel
Harnessing deep learning for population genetic inference
Autor*in
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Abstract
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
Stichwort
Genetic variationMachine learning
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
phaidra.univie.ac.at/o:2046383
Erschienen in
Titel
Nature Reviews Genetics
Band
25
ISSN
1471-0056
Erscheinungsdatum
2023
Seitenanfang
61
Seitenende
78
Publication
Springer Science and Business Media LLC
Version
Fördergeber
Verfügbarkeitsdatum
04.03.2024
Datum der Annahme zur Veröffentlichung
2023
Zugänglichkeit
Rechte
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