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...

Full beskrivning

Sparad:
Bibliografiska uppgifter
I publikationen:BioData Min
Huvudupphovsmän: 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.
Materialtyp: Artigo
Språk:Inglês
Publicerad: BioMed Central 2018
Ämnen:
Research
Länkar: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
Taggar: Lägg till en tagg
Inga taggar, Lägg till första taggen!

Liknande verk