학술논문
Integration of genome-scale data identifies candidate sleep regulators
Original Article
Original Article
Document Type
Academic Journal
Author
Source
SLEEP. February 2023, Vol. 46 Issue 2, p1N, 15 p.
Subject
Language
English
ISSN
0161-8105
Abstract
Introduction Genetics impacts sleep. In humans, a handful of alleles are known to cause familial sleep disorders [1-8]. However, most of these alleles are rare and have not been broadly [...]
Study Objectives: 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. Methods: 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. Results: 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-[kappa]B pathway and showed that chronic activation of NF-[kappa]B in a genetic mouse model impacted the sleep-wake patterns. Conclusion: 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. Key words: sleep regulation; genetics; machine learning; genome-scale data integration
Study Objectives: 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. Methods: 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. Results: 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-[kappa]B pathway and showed that chronic activation of NF-[kappa]B in a genetic mouse model impacted the sleep-wake patterns. Conclusion: 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. Key words: sleep regulation; genetics; machine learning; genome-scale data integration