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Exploring potential gene signatures in dengue through machine learning and deep learning approaches.

Jhansi Venkata Nagamani Josyula1,2, Shraddha Jangili1,2, Nikhila Yaladanda1,2

  • 1Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.

Virus Genes
|December 2, 2025
PubMed
Summary

This study identifies key genes for diagnosing dengue fever and severe dengue using machine learning on microarray data. These identified genes, particularly those involved in platelet function, show promise as diagnostic biomarkers.

Keywords:
Bioinformatics analysisBiomarker predictionDeep learningDengueFeature selectionMachine learningMicroarray data

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Area of Science:

  • Genomics
  • Bioinformatics
  • Infectious Diseases

Background:

  • Dengue poses a significant global public health challenge.
  • Accurate diagnosis is crucial for effective patient management.

Purpose of the Study:

  • To identify differentially expressed genes (DEGs) associated with dengue clinical conditions.
  • To discover potential diagnostic biomarkers for dengue using advanced computational methods.

Main Methods:

  • Analysis of public microarray datasets (GSE84331, GSE18090, GSE43777, E-MTAB-3162).
  • Application of statistical analysis, Machine Learning (ML), and Deep Learning (DL) techniques, including Random Forest and Support Vector Machine with Genetic Algorithm (SVM-GA).
  • Functional enrichment, platelet signaling, and protein-protein interaction (PPI) network analysis.

Main Results:

  • Identified 27 DEGs in dengue fever (DF) vs. control (C) and 13 DEGs in severe dengue (SD) vs. DF using Random Forest.
  • Identified 79 DEGs in SD vs. C using SVM-GA.
  • Highlighted seven hub genes (PIK3R1, GATA3, ZFPM, SKAP1, TP63, ZBTB20, ZEB2) with potential roles in hemostasis and platelet function.

Conclusions:

  • The identified hub genes show potential as diagnostic markers for dengue.
  • Further validation in larger cohorts is necessary to confirm their prognostic utility.