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Related Concept Videos

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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.
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Related Experiment Video

Updated: Nov 19, 2025

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HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets.

Shilan S Hameed1,2, Rohayanti Hassan3, Wan Haslina Hassan1

  • 1Computer Systems and Networks (CSN), Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.

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Summary

A new application, HDG-select, aids researchers in selecting and classifying disease-related genes using machine learning. This user-friendly tool offers competitive performance and accessibility for high-dimensional genomic data analysis.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Machine Learning in Healthcare

Background:

  • Gene selection and classification are critical for identifying disease-associated genes.
  • Biomedical researchers require user-friendly tools integrating statistical and machine learning methods for high-dimensional data.
  • Existing tools may lack comprehensive functionality or user accessibility.

Purpose of the Study:

  • To develop a novel, stand-alone graphical user interface (GUI) application for gene selection and classification.
  • To provide a user-friendly tool with robust statistical and machine learning capabilities for high-dimensional datasets.
  • To validate the application's performance against existing methods.

Main Methods:

  • Development of a novel stand-alone application named HDG-select.
  • Implementation of a graphical user interface (GUI) for enhanced usability.
  • Utilized a combined filter-GBPSO-SVM algorithm for efficient gene selection and classification in high-dimensional data.

Main Results:

  • HDG-select was validated on eleven diverse high-dimensional datasets (CSV and GEO soft formats).
  • The application demonstrated superior performance compared to other reported tools in literature.
  • HDG-select offers competitive performance, accessibility, and comprehensive functionality for gene analysis.

Conclusions:

  • The developed HDG-select application effectively addresses the need for a user-friendly tool for gene selection and classification.
  • The application's performance and accessibility make it a valuable resource for biomedical researchers.
  • HDG-select provides an efficient and reliable solution for analyzing high-dimensional genomic data.