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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Updated: Jun 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Accurate and robust gene selection for disease classification using a simple statistic.

Hikaru Mutsubayashi1, Seiichiro Aso, Tomomasa Nagashima

  • 1Division of Production and Information Systems Engineering, Muroran Institute of Technology, Mizumoto, Muroran, Japan.

Bioinformation
|February 25, 2009
PubMed
Summary
This summary is machine-generated.

Identifying key genes for disease classification is vital. This study introduces a gene selection method using forward variable selection (FSM) to accurately identify crucial genes from microarray data for improved disease diagnosis.

Keywords:
DNA chipdiseasegene selectionmicroarrayrobust

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Last Updated: Jun 25, 2026

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate disease classification using gene expression data is critical in clinical settings.
  • Identifying a small subset of informative genes from large datasets (microarrays, DNA chips) is a significant challenge.

Purpose of the Study:

  • To develop and evaluate a gene selection method for identifying discriminative genes from microarray data.
  • To improve the accuracy of disease classification by extracting a minimal yet crucial set of genes.

Main Methods:

  • Utilized a forward variable (gene) selection method (FSM).
  • Applied the method to public microarray datasets for validation.

Main Results:

  • The proposed method successfully extracted a small subset of highly informative genes.
  • Achieved very high accuracy in discriminating between different disease classes, approaching perfect classification.

Conclusions:

  • The FSM-based gene selection method is effective for identifying key genes in disease classification.
  • This approach offers a promising strategy for accurate and efficient disease diagnosis using gene expression data.