Genome wide association studies are now widely used in the livestock sector to estimate the association among single nucleotide polymorphisms (SNPs) distributed across the whole genome and one or more trait. As computational power increases, the use of machine learning techniques to analyze large genome wide datasets becomes possible.
Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach / G. Minozzi, A. Pedretti, S. Biffani, E.L. Nicolazzi, A. Stella. - In: BMC PROCEEDINGS. - ISSN 1753-6561. - 8:suppl. 5(2014 Oct 07), pp. S4.1-S4.6. [10.1186/1753-6561-8-S5-S4]
Genome wide association analysis of the 16th QTL- MAS Workshop dataset using the Random Forest machine learning approach
G. Minozzi;
2014
Abstract
Genome wide association studies are now widely used in the livestock sector to estimate the association among single nucleotide polymorphisms (SNPs) distributed across the whole genome and one or more trait. As computational power increases, the use of machine learning techniques to analyze large genome wide datasets becomes possible.
| File | Dimensione | Formato | |
|---|---|---|---|
|
1753-6561-8-S5-S4.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Dimensione
217.38 kB
Formato
Adobe PDF
|
217.38 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
