Integration of genome-scale data identifies candidate sleep regulators.
genetics
genome-scale data integration
machine learning
sleep regulation
Journal
Sleep
ISSN: 1550-9109
Titre abrégé: Sleep
Pays: United States
ID NLM: 7809084
Informations de publication
Date de publication:
08 02 2023
08 02 2023
Historique:
received:
21
06
2022
revised:
02
09
2022
pubmed:
4
12
2022
medline:
10
2
2023
entrez:
3
12
2022
Statut:
ppublish
Résumé
Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. In this study, we built machine learning models to predict sleep genes based on their similarity to genes that are known to regulate sleep. We trained a prediction model on thousands of published datasets, representing circadian, immune, sleep deprivation, and many other processes, using a manually curated list of 109 sleep genes. Our predictions fit with prior knowledge of sleep regulation and identified key genes and pathways to pursue in follow-up studies. As an example, we focused on the NF-κB pathway and showed that chronic activation of NF-κB in a genetic mouse model impacted the sleep-wake patterns. Our study highlights the power of machine learning in integrating prior knowledge and genome-wide data to study genetic regulation of complex behaviors such as sleep.
Identifiants
pubmed: 36462188
pii: 6871057
doi: 10.1093/sleep/zsac279
pmc: PMC9905783
pii:
doi:
Substances chimiques
NF-kappa B
0
Banques de données
figshare
['10.6084/m9.figshare.20517951']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHLBI NIH HHS
ID : K08 HL148551
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS054794
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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