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Rapid, Reference-Free human genotype imputation with denoising autoencoders.

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第一作者： Raquel,Dias

第一单位： Scripps Research Translational Institute, Scripps Research Institute, La Jolla, United States.;Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United States.;Department of Microbiology and Cell Science, University of Florida, Gainesville, United States.

作者： Raquel,Dias ^[1] ; Doug,Evans ^[2] ; Shang-Fu,Chen ^[2] ; Kai-Yu,Chen ^[2] ; Salvatore,Loguercio ^[2] ; Leslie,Chan ^[2] ; Ali,Torkamani ^[2]

作者单位： Scripps Research Translational Institute, Scripps Research Institute, La Jolla, United States.;Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United States.;Department of Microbiology and Cell Science, University of Florida, Gainesville, United States. ^[1] Scripps Research Translational Institute, Scripps Research Institute, La Jolla, United States.;Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, United States. ^[2]

关键词 artifitial intelligence autoencoder computational biology deep learning genetics genomics human imputation population genetics systems biology

医学主题词基因频率(Gene Frequency);遗传学, 群体(Genetics, Population);全基因组关联研究(Genome-Wide Association Study);基因型(Genotype);人类(Humans);连锁不平衡(Linkage Disequilibrium);多态性, 单核苷酸(Polymorphism, Single Nucleotide)

DOI 10.7554/eLife.75600

PMID 36148981

发布时间 2024-09-23

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准确一般字义偏差语意偏离译文作用小提交