机器学习推动了暴饮暴食症的基因发现。

IF 31.7 1区生物学 Q1 GENETICS & HEREDITY

Nature genetics Pub Date : 2023-08-07 DOI:10.1038/s41588-023-01473-0

Jackson G. Thorp, Zachary F. Gerring, Eske M. Derks

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引用次数: 0

摘要

确定暴饮暴食症（BED）的遗传风险因素对于了解其病因并制定有效的预防和干预策略至关重要。为了克服临床暴食症诊断报告不足的问题，一项新的研究利用机器学习技术鉴定了与暴食症定量风险评分相关的遗传变异，并发现了血红素代谢在病理上发挥作用的证据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine learning drives genetic discovery for binge eating disorder

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Machine learning drives genetic discovery for binge eating disorder

Identifying genetic risk factors for binge-eating disorder (BED) is vital to understand its etiology and develop effective prevention and intervention strategies. To overcome under-reporting of clinical BED diagnosis, a new study uses machine learning to identify genetic variants associated with quantitative BED risk scores and finds evidence for a pathological role of heme metabolism.

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来源期刊

Nature genetics 生物-遗传学

CiteScore

43.00

自引率

2.60%

发文量

241

审稿时长

3 months

期刊介绍： Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation. Integrative genetic topics comprise, but are not limited to: -Genes in the pathology of human disease -Molecular analysis of simple and complex genetic traits -Cancer genetics -Agricultural genomics -Developmental genetics -Regulatory variation in gene expression -Strategies and technologies for extracting function from genomic data -Pharmacological genomics -Genome evolution