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Updated: Jun 30, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Identifying differentially expressed subnetworks with MMG.

Josselin Noirel1, Guido Sanguinetti, Phillip C Wright

  • 1Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK. j.noirel@sheffield.ac.uk

Bioinformatics (Oxford, England)
|September 30, 2008
PubMed
Summary
This summary is machine-generated.

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Mixture model on graphs (MMG) offers robust gene and protein regulation prediction, even with missing data. A new C/R implementation enhances speed and usability for quantitative proteomics research.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Mixture model on graphs (MMG) is a probabilistic framework integrating network topology with gene and protein expression data.
  • MMG excels in predicting gene and protein regulation states.
  • Its robustness to missing data is crucial for quantitative proteomics.

Purpose of the Study:

  • To introduce a new, efficient implementation of the Mixture model on graphs (MMG).
  • To enhance the speed, usability, and extensibility of MMG for biological network analysis.

Main Methods:

  • A novel implementation of MMG developed in C.
  • The C implementation is interfaced with the R statistical programming environment.
  • The model integrates network topology with expression data.

Related Experiment Videos

Last Updated: Jun 30, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Main Results:

  • The new MMG implementation demonstrates significantly improved speed.
  • Enhanced ease of use and extensibility for researchers.
  • Robust performance in handling missing data in biological networks.

Conclusions:

  • The new C/R implementation of MMG provides a powerful and accessible tool for analyzing gene and protein regulation.
  • This advancement facilitates more effective quantitative proteomics studies.
  • The software is available through CRAN and BioConductor.