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Related Experiment Video

Updated: May 20, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Published on: May 16, 2022

Sparse regularized discriminant analysis with application to microarrays.

Ran Li¹, Baolin Wu

¹Division of Biostatistics, School of Public Health University of Minnesota, Minneapolis, MN 55455, USA.

Computational Biology and Chemistry

|July 24, 2012

Summary

This study introduces a unified method for cancer prediction using gene expression data. The approach simultaneously selects key genes and models their interactions for improved accuracy.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Last Updated: May 20, 2026

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

Published on: May 16, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer prediction models often benefit from incorporating gene interactions.
Simultaneously selecting important genes and modeling these interactions presents a significant challenge.

Purpose of the Study:

To develop a unified modeling approach for simultaneous gene selection and interaction modeling in cancer prediction.
To evaluate the performance of this new approach using simulations and real-world data.

Main Methods:

Utilized a unified modeling approach based on penalized likelihood estimation.
Employed ℓ(1) regularization to facilitate simultaneous gene selection and interaction modeling.

Main Results:

The proposed method demonstrated competitive performance in simulation studies.
The approach was successfully applied to public microarray data for cancer prediction.

Conclusions:

The unified modeling approach effectively handles simultaneous gene selection and interaction modeling.
This method offers a promising strategy for improving cancer prediction using large-scale gene expression data.