A Novel Filter and Embedded Feature Selection Methods applied to High Dimensional Metabolomics Data in Enhancing Colorectal Cancer Classification
Nurain Ibrahim,
Ahmad Zia Ul-Saufie,
Kukatharmini Tharmaratnam
et al.
Abstract:Background
Metabolomics is an emerging field, which focuses on the study of small molecules (metabolites) and their chemical processes. Metabolomics data are highly dimensional, with p>>n where p is the number of variables and n is the sample size of the cohort. Hence, feature selection is a key step in metabolomics studies to reduce the dimensionality in the dataset, removing redundant and unwanted features and mitigating overfitting. The t-test (T) and correlation sharing t-test method (corT) can be us… Show more
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