Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

Journal: International immunopharmacology
PMID: 39847951

Abstract

INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

Authors

  • Masoud Arabfard
    Department of Bioinformatics, Kish International Campus University of Tehran, Kish, Iran.
  • Esmaeil Behmard
    School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran.
  • Mazaher Maghsoudloo
    Key Laboratory of Epigenetics and Oncology, the Research Center for Preclinical Medicine, Southwest Medical University, Luzhou, Sichuan, China.
  • Emad Dadgar
    Students' Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Shahram Parvin
    Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
  • Hasan Bagheri
    Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Postal code 1435916-471, Iran.

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