In:
Biological Chemistry, Walter de Gruyter GmbH, Vol. 402, No. 8 ( 2021-07-27), p. 871-885
Abstract:
Using results from genome-wide association studies for understanding complex traits is a current challenge. Here we review how genotype data can be used with different machine learning (ML) methods to predict phenotype occurrence and severity from genotype data. We discuss common feature encoding schemes and how studies handle the often small number of samples compared to the huge number of variants. We compare which ML methods are being applied, including recent results using deep neural networks. Further, we review the application of methods for feature explanation and interpretation.
Type of Medium:
Online Resource
ISSN:
1431-6730
,
1437-4315
DOI:
10.1515/hsz-2021-0109
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2021
detail.hit.zdb_id:
1466062-3
SSG:
12
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