Regularized Machine Learning in the Genetic Prediction of Complex Traits

[...]we discuss some key future advances, open questions and challenges in this developing field, when moving toward low-frequency variants and cross-phenotype interactions. Multivariate modeling approaches have already been shown to provide improved insights into genetic mechanisms and the intera...

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Podrobná bibliografia
Vydané v:	PLoS genetics Ročník 10; číslo 11; s. e1004754
Hlavní autori:	Okser, Sebastian, Pahikkala, Tapio, Airola, Antti, Salakoski, Tapio, Ripatti, Samuli, Aittokallio, Tero
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 01.11.2014 Public Library of Science (PLoS)
Predmet:	Artificial Intelligence Biology and Life Sciences Cardiovascular disease Computer and Information Sciences Datasets Genetic Association Studies Genetic research Genome-Wide Association Study Genomes Genotype & phenotype Health risk assessment Humans Machine learning Medicine and Health Sciences Models, Statistical Physical Sciences Quantitative trait loci Review
ISSN:	1553-7404, 1553-7390, 1553-7404
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Popis
Shrnutí:	[...]we discuss some key future advances, open questions and challenges in this developing field, when moving toward low-frequency variants and cross-phenotype interactions. Multivariate modeling approaches have already been shown to provide improved insights into genetic mechanisms and the interaction networks behind many complex traits, including atherosclerosis, coronary heart disease, and lipid levels, which would have gone undetected using the standard univariate modeling [2], [19]-[22].
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7404 1553-7390 1553-7404
DOI:	10.1371/journal.pgen.1004754