Ensemble outlier detection and gene selection in triple-negative breast cancer data

Abstract Background Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, e...

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Detalhes bibliográficos

Principais autores:	Marta B. Lopes, André Veríssimo, Eunice Carrasquinha, Sandra Casimiro, Niko Beerenwinkel, Susana Vinga
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2018-05-01
coleção:	BMC Bioinformatics
Assuntos:	Ensemble modeling High-dimensionality Outlier detection Rank Product test Triple-negative breast cancer
Acesso em linha:	http://link.springer.com/article/10.1186/s12859-018-2149-7
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