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Dynamic Gene Attention Focus (DyGAF): Enhancing Biomarker Identification Through Dual-Model Attention Networks.

Md Khairul Islam1,2, Himanshu Wagh3, Hairong Wei1,2,3

  • 1Computational Science and Engineering, Michigan Technological University, Houghton, MI, USA.

Bioinformatics and Biology Insights
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

The novel Dynamic Gene Attention Focus (DyGAF) model accurately identifies key genes for disease diagnosis and prognosis. This AI tool achieved 94.23% accuracy in diagnosing COVID-19, offering a significant advancement in genomic research.

Keywords:
COVID-19RNA-seqattention models; gene ontologybiomarkersclassificationdeep learningmachine learningpathogenesispathways

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Biomarker discovery and disease diagnostics are critical challenges in pathogen infection.
  • Traditional methods for analyzing gene expression data often lack the precision needed for complex diseases.
  • Understanding host-pathogen interactions requires advanced computational tools for gene analysis.

Purpose of the Study:

  • To introduce and validate the Dynamic Gene Attention Focus (DyGAF) model for enhanced biomarker detection and disease diagnostics.
  • To identify and rank significant genes associated with disease progression and prognosis.
  • To apply the DyGAF model to COVID-19 gene expression data for diagnostic and pathogenic insights.

Main Methods:

  • Development of a novel dual-model attention-based mechanism within neural networks.
  • Integration of machine learning algorithms for gene identification and ranking.
  • Analysis of gene expression profiles, including KEGG pathways, Wiki Pathways, and Gene Ontology.
  • Validation against differential expression and random forest methods using COVID-19 nasopharyngeal swab data.

Main Results:

  • DyGAF successfully identified and ranked key genes crucial for disease detection and prognosis.
  • The model provided a multileveled evaluation of gene roles through pathway and ontology analyses.
  • DyGAF achieved 94.23% accuracy in classifying COVID-19 gene-expression profiles.
  • Benchmarking confirmed DyGAF's superior performance compared to conventional models.

Conclusions:

  • The DyGAF model represents a significant advancement in genomic research for identifying genetic markers.
  • It offers a more comprehensive and precise tool for unraveling complex biological insights of diseases.
  • DyGAF enhances biomarker discovery, disease progression reporting, and diagnostic capabilities.