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