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Updated: May 2, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Published on: January 13, 2016

Selecting informative genes for discriminant analysis using multigene expression profiles.

Xin Yan¹, Tian Zheng

¹Russell Investments, Tacoma, WA, USA. XYan@russell.com

|October 10, 2008

Summary

This summary is machine-generated.

This study introduces novel feature selection methods for gene expression analysis, improving classification accuracy by considering gene-gene interactions. These methods identify informative genes that single-gene tests might miss, enhancing discriminant analysis.

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Last Updated: May 2, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

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Published on: January 13, 2016

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Published on: October 11, 2018

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression data from microarrays offer high-dimensional spaces for sample classification.
High-dimensional gene expression data often contain non-informative features that introduce noise.
Effective feature selection is crucial for accurate discriminant analysis in high-dimensional biological data.

Purpose of the Study:

To develop and evaluate novel feature selection methods for gene expression data.
To assess the utility of multigene expression profiles and gene-gene interactions in feature selection.
To improve the classification power of gene expression analysis.

Main Methods:

Proposed and studied feature selection methods evaluating gene "informativeness".
Employed two information measures based on multigene expression profiles.
Utilized a backward information-driven screening approach for gene feature selection.
Considered gene-gene interaction information within multigene expression profiles.

Main Results:

Demonstrated improved classification power by incorporating gene-gene interactions.
Identified important genes with weak marginal but strong interaction information.
Compared proposed methods against existing approaches using breast cancer data.

Conclusions:

Methods considering gene-gene interactions enhance classification power in gene expression analysis.
Novel feature selection strategies can identify crucial genes overlooked by single-gene analyses.
The proposed approach offers a more robust method for feature selection in high-dimensional genomic studies.