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

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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Bibliographic Details
Main Authors: Marta B. Lopes, André Veríssimo, Eunice Carrasquinha, Sandra Casimiro, Niko Beerenwinkel, Susana Vinga
Format: Article
Language:English
Published: BMC 2018-05-01
Series:BMC Bioinformatics
Subjects:
Ensemble modeling
High-dimensionality
Outlier detection
Rank Product test
Triple-negative breast cancer
Online Access:http://link.springer.com/article/10.1186/s12859-018-2149-7

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http://link.springer.com/article/10.1186/s12859-018-2149-7

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