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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Detalles Bibliográficos
Autores principales: 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: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907720/
https://www.ncbi.nlm.nih.gov/pubmed/29713383
http://dx.doi.org/10.1186/s13040-018-0168-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907720/
https://www.ncbi.nlm.nih.gov/pubmed/29713383
http://dx.doi.org/10.1186/s13040-018-0168-6

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