Collective feature selection to identify crucial epistatic variants

BACKGROUND: Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample...

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Detalhes bibliográficos
Publicado no:BioData Min
Main Authors: Verma, Shefali S., Lucas, Anastasia, Zhang, Xinyuan, Veturi, Yogasudha, Dudek, Scott, Li, Binglan, Li, Ruowang, Urbanowicz, Ryan, Moore, Jason H., Kim, Dokyoon, Ritchie, Marylyn D.
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2018
Assuntos:
Research
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC5907720/
https://ncbi.nlm.nih.gov/pubmed/29713383
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13040-018-0168-6
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