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Related Concept Videos

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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Published on: July 1, 2020

FiGS: a filter-based gene selection workbench for microarray data.

Taeho Hwang1, Choong-Hyun Sun, Taegyun Yun

  • 1Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea.

BMC Bioinformatics
|January 27, 2010
PubMed
Summary
This summary is machine-generated.

Finding the best gene selection method for disease diagnosis from microarray data is challenging. FiGS (Feature Importance Gene Selection) is a web tool that automates this process, comparing multiple methods to identify optimal diagnostic biomarkers.

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Last Updated: Jun 16, 2026

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07:35

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Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene selection from microarray data is crucial for identifying diagnostic biomarkers.
  • Existing methods vary in performance across different datasets.
  • A comparative approach is needed to find optimal gene selection strategies.

Purpose of the Study:

  • To develop an automated system for comparing diverse gene selection methods.
  • To identify the optimal gene selection results for specific microarray datasets.
  • To provide an efficient and comprehensive tool for biomarker discovery.

Main Methods:

  • FiGS, a web-based workbench, aligns various feature selection techniques and classifiers.
  • Incorporates gene clustering and data pre-processing options to diversify methods.
  • Evaluates candidate procedures using .632+ bootstrap errors and classification accuracy.
  • Utilizes parallel computing for efficient, heavy computations.

Main Results:

  • FiGS automatically compares multiple gene selection procedures.
  • It identifies and provides the optimal gene selection results for input microarray data.
  • Selected gene sets and their classification accuracies are listed.

Conclusions:

  • FiGS automates extensive gene selection analysis in a parallel computing environment.
  • It offers an efficient and comprehensive solution for identifying disease-discriminating gene sets.
  • The tool facilitates biomarker discovery from microarray data.