Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies

6533b861fe1ef96bd12c580f

RESEARCH PRODUCT

Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies

Sören Sonnenburg Thorsten Dickhaus Ernst Fehr Daniel Schunk Klaus-robert Müller Klaus-robert Müller Marius Kloft Xavier Farré Juan Antonio Rodríguez Urko M. Marigorta Bettina Mieth Robin Vobruba Gilles Blanchard Carlos Morcillo-suarez Arcadi Navarro Arcadi Navarro

subject

0301 basic medicine Statistical methods Computer science Genome-wide association study Machine learning computer.software_genre Genome-wide association studies Statistical power Article [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Set (abstract data type)03 medical and health sciences [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]10007 Department of Economics Statistical significance Replication (statistics)genome Statistical hypothesis testing Genetic association 1000 Multidisciplinary Multidisciplinary business.industry Computational science Institut für Mathematik 330 Economics Support vector machine 030104 developmental biology Multiple comparisons problem wide association studies statistical methods Artificial intelligence business computer

description

Mieth, Bettina et al.

year	journal	country	edition	language
2016-11-28

10.5167/uzh-130300 https://www.zora.uzh.ch/id/eprint/130300/

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