A balanced iterative random forest for gene selection from microarray data

BACKGROUND: The wealth of gene expression values being generated by high throughput microarray technologies leads to complex high dimensional datasets. Moreover, many cohorts have the problem of imbalanced classes where the number of patients belonging to each class is not the same. With this kind o...

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Detalles Bibliográficos
Autores principales: Anaissi, Ali, Kennedy, Paul J, Goyal, Madhu, Catchpoole, Daniel R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766035/
https://www.ncbi.nlm.nih.gov/pubmed/23981907
http://dx.doi.org/10.1186/1471-2105-14-261

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766035/
https://www.ncbi.nlm.nih.gov/pubmed/23981907
http://dx.doi.org/10.1186/1471-2105-14-261

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