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Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests
BACKGROUND: Single-nucleotide polymorphisms (SNPs) selection and identification are the most important tasks in Genome-wide association data analysis. The problem is difficult because genome-wide association data is very high dimensional and a large portion of SNPs in the data is irrelevant to the d...
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| Publicado no: | BMC Genomics |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2015
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4331719/ https://ncbi.nlm.nih.gov/pubmed/25708662 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2164-16-S2-S5 |
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