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.
Random Forest ; QTL ; Genomica
Settore AGR/17 - Zootecnica Generale e Miglioramento Genetico
Article (author)
File in questo prodotto:
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

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/251682
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 5
  • ???jsp.display-item.citation.isi??? ND
social impact