Integrated epigenomic exposure signature discovery. Academic Article uri icon

Overview

abstract

  • Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

authors

publication date

  • September 3, 2024

Research

keywords

  • COVID-19
  • Epigenomics
  • Machine Learning
  • Staphylococcus aureus

Identity

PubMed Central ID

  • PMC11404615

Scopus Document Identifier

  • 85203018679

Digital Object Identifier (DOI)

  • 10.1080/17501911.2024.2375187

PubMed ID

  • 39225561

Additional Document Info

volume

  • 16

issue

  • 14