Article

Variable selection methods for classification : application to metabolomics data

2020-01-01

Abstract excerpt

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. Variable selection is therefore a key step in metabolomics studies. There are three categories of variable selection, such as filter, wrapper and embedded methods. Common univar...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b21c85f6-b257-5df0-a448-6a2808691c0d
DOI: 10.17638/03093506

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Variable selection methods for classification : application to metabolomics dataDOI 10.17638/03093506Read

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical dataSemantic proximity Read

Comparison of classification accuracy and feature selection between sparse and non-sparse modeling of metabolomics dataSemantic proximity Read

Variable-Selection ANOVA Simultaneous Component Analysis (VASCA)Semantic proximity Read

The usefulness of sparse k-means in metabolomics data: An example from breast cancer dataSemantic proximity Read

A new statistical workflow (R-packages based) to investigate associations between one variable of interest and the metabolome.Semantic proximity Read

Primal-Dual for Classification with Rejection (PD-CR): A Novel Method for Classification and Feature Selection. An Application in Metabolomics Studies.Semantic proximity Read

Evaluation of classifier performance for multiclass phenotype discrimination in untargeted metabolomicsSemantic proximity Read

A Benchmarking Study of Feature Screening Approaches Across Omics Classification SettingsSemantic proximity Read

Data-adaptive pipeline for filtering and normalizing metabolomics dataSemantic proximity Read

PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraintsSemantic proximity Read

A novel variable selection method for classification with application to single nucleotide polymorphism dataSemantic proximity Read

Efficient feature selection on gene expression data: Which algorithm to use?Semantic proximity Read