Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes.

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作者： Remo,Monti ^[1] ; Pia,Rautenstrauch ^[2] ; Mahsa,Ghanbari ^[2] ; Alva Rani,James ^[3] ; Matthias,Kirchler ^[2] ; Uwe,Ohler ^[1] ; Stefan,Konigorski ^[1] ; Christoph,Lippert ^[4]

作者单位： Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany. ^[1] Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 13125, Berlin, Germany. ^[2] Humboldt-Universität zu Berlin, Department of Computer Science, 10099, Berlin, Germany. ^[3] TU Kaiserslautern, Department of Computer Science, 67663, Kaiserslautern, Germany. ^[4] Humboldt-Universität zu Berlin, Department of Biology, 10099, Berlin, Germany. ^[5] Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, 10029-6574, NY, USA. ^[6] Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany. christoph.lippert@hpi.de. ^[7] Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, 10029-6574, NY, USA. christoph.lippert@hpi.de. ^[8]

主题词遗传关联研究(Genetic Association Studies);遗传标记(Genetic Markers);人类(Humans);离子通道(Ion Channels);突变, 误义(Mutation, Missense)

DOI 10.1038/s41467-022-32864-2

PMID 36088354

发布时间 2023-03-08

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