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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Vydáno v:PLoS genetics Ročník 10; číslo 11; s. e1004754
Hlavní autoři: Okser, Sebastian, Pahikkala, Tapio, Airola, Antti, Salakoski, Tapio, Ripatti, Samuli, Aittokallio, Tero
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 01.11.2014
Public Library of Science (PLoS)
Témata:
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
On-line přístup:Získat plný text
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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].
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1004754