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Identifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model.

Jaehyun Park1,2, Sungho Won1,2,3,4

  • 1Interdisciplinary Program of Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.

Genetic Epidemiology
|January 15, 2025
PubMed
Summary

A new method, fused mixed graphical model (FMGM), identifies network structures for diseases like atopic dermatitis. FMGM shows superior performance in network inference using multiomics data, highlighting metabolic pathways.

Keywords:
Markov random fieldatopic dermatitis (AD)microbiomemixed datamultiomicsnetwork inferencetranscriptome

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Area of Science:

  • Computational Biology
  • Genomics
  • Network Science

Background:

  • Understanding complex diseases requires analyzing multiomics data to infer biological network structures.
  • Atopic dermatitis (AD) is a complex condition influenced by genetic and environmental factors, necessitating advanced analytical methods.

Purpose of the Study:

  • To introduce a novel method, fused mixed graphical model (FMGM), for inferring network structures associated with dichotomous phenotypes.
  • To apply FMGM to identify multiomics profiles and network structures related to atopic dermatitis in infants.

Main Methods:

  • Developed FMGM, a method based on a pairwise Markov random field model.
  • Evaluated FMGM's performance using synthetic datasets with power-law networks through simulations.
  • Applied FMGM to multiomics data from infants to identify associations with atopic dermatitis.

Main Results:

  • FMGM demonstrated superior performance in network inference compared to the previous method, causalMGM, achieving higher F1-scores (0.730 vs. 0.550).
  • Identified a significant association between carotenoid biosynthesis and RNA degradation in relation to AD.
  • Highlighted the potential importance of metabolism, oxidative stress, and microbial RNA balance in AD pathogenesis.

Conclusions:

  • FMGM is an effective tool for inferring network structures from multiomics data, outperforming existing methods.
  • The findings suggest key metabolic pathways and molecular processes potentially involved in the development of atopic dermatitis.
  • The study provides an R package 'fusedMGM' and example data for reproducible research.