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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Summarizing cellular responses as biological process networks.

Christopher D Lasher1, Padmavathy Rajagopalan, T M Murali

  • 1Genetics, Bioinformatics, and Computational Biology Ph.D. Program, Virginia Tech, Blacksburg, VA 24061 USA.

BMC Systems Biology
|July 31, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Markov chain Monte Carlo Biological Process Networks (MCMC-BPN) to identify key biological process links from complex gene expression data. MCMC-BPN effectively reduces redundancy and highlights significant biological trends.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Microarray experiments identify thousands of perturbed genes.
  • Response networks integrate gene interactions and expression data, yielding complex results.
  • Current gene set enrichment methods can produce numerous overlapping functions.

Purpose of the Study:

  • To develop a novel technique for identifying non-redundant links between biological processes.
  • To effectively summarize complex response networks in a biologically relevant manner.

Main Methods:

  • Developed Markov chain Monte Carlo Biological Process Networks (MCMC-BPN).
  • MCMC-BPN identifies a non-redundant set of links between perturbed biological processes.
  • Applied MCMC-BPN to liver-related gene expression datasets.

Main Results:

  • MCMC-BPN reports highly non-redundant links between biological processes.
  • Networks formed by MCMC-BPN links show high relevance to biological conditions.
  • MCMC-BPN effectively discerns key inter-process links from a large solution space.

Conclusions:

  • MCMC-BPN successfully explains perturbed gene-gene interactions using a minimal set of inter-process links.
  • Biological Process Networks (BPNs) generated by MCMC-BPN offer a digestible summary of important biological trends.
  • These summarized links facilitate detailed exploration of biological responses.