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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
270

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A Rat Methyl-Seq Platform to Identify Epigenetic Changes Associated with Stress Exposure
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Integrated epigenomic exposure signature discovery.

Jared Schuetter1, Angela Minard-Smith1, Brandon Hill2

  • 1Health Business Unit, Battelle Memorial Institute, Columbus, OH 43201, USA.

Epigenomics
|September 3, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning identifies epigenetic signatures for various exposures, aiding in diagnosis and forensic attribution. This approach enables the development of diagnostic panels for precision health.

Keywords:
diagnosticsepigenomicsexposure healthinfectionmachine learningmulti-omicstranscriptomics

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Area of Science:

  • Epigenetics and computational biology.
  • Machine learning applications in health sciences.

Background:

  • The epigenome plays a crucial role in gene regulation and phenotype modification in response to environmental exposures.
  • Epigenome assessment offers a potential method for determining past exposure history, which can aid in medical diagnosis.

Purpose of the Study:

  • To develop and implement a machine learning algorithm for identifying key features in epigenomic and transcriptomic data.
  • To create integrated exposure signatures (ESs) from multiple datasets.

Main Methods:

  • Developed the Exposure Signature Discovery Algorithm (ESDA), a machine learning approach.
  • Applied ESDA to multiple epigenomic and transcriptomic datasets to identify significant features.
  • Generated integrated exposure signatures (ESs) for various exposures.

Main Results:

  • Successfully developed ESs for seven distinct exposures, including bacterial and viral agents (Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A H3N2, Bacillus anthracis vaccinations).
  • Observed variations in the assays, selected features, and predictive values among the developed ESs.
  • Demonstrated that ESDA effectively identifies distinguishing features for each exposure.

Conclusions:

  • Integrated exposure signatures hold potential for diagnostic purposes and forensic attribution.
  • The ESDA facilitates the identification of critical biomarkers, paving the way for diagnostic panel development.
  • This methodology supports future advancements in precision health by enabling targeted diagnostic tools.