TAILIEUCHUNG - Báo cáo sinh học: "Comparison of classification methods for detecting associations between SNPs and chick mortalit"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Comparison of classification methods for detecting associations between SNPs and chick mortalit | Genetics Selection Evolution BioMed Central Research Comparison of classification methods for detecting associations between SNPs and chick mortality Nanye Long 1 Daniel Gianola1 2 Guilherme JM Rosa2 Kent A Weigel2 and Santiago Avendano3 Address Department of Animal Sciences University of Wisconsin Madison WI 53706 USA 2Department of Dairy Science University of Wisconsin Madison WI 53706 USA and 3Aviagen Ltd. Newbridge Midlothian EH28 8SZ UK Email Nanye Long - nlong@ Daniel Gianola-gianola@ Guilherme JM Rosa-grosa@ Kent AWeigel - kweigel@ Santiago Avendano - savendano@ Corresponding author Open Access Published 23 January 2009 Received 17 December 2008 Genetics Selection Evolution 2009 41 18 doi l297-9686-4l-l8 Accepted 23 January 2009 This article is available from http content 4l l l8 2009 Long et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories 2 3 4 5 and 10 and three classification algorithms naive Bayes classifiers Bayesian networks and neural networks were compared using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naive Bayes classifier coupled with discretization into two or three categories .

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