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Efficient proximal gradient algorithm for inference of differential gene networks.

Chen Wang1, Feng Gao1, Georgios B Giannakis2

  • 1Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA.

BMC Bioinformatics
|May 4, 2019
PubMed
Summary
This summary is machine-generated.

We developed ProGAdNet, a new algorithm that jointly infers gene networks to accurately identify differential network changes. This method outperforms existing approaches and aids in understanding cancer-related gene interactions.

Keywords:
Differential networkGene networkProximal gradient method

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene networks dynamically change across various biological conditions, including disease states.
  • Identifying these differential gene network changes is crucial for understanding biological processes.
  • Existing methods infer networks separately, missing potential similarities and leading to suboptimal results.

Purpose of the Study:

  • To develop a novel algorithm for joint inference of gene networks under different conditions.
  • To accurately identify differential changes in gene network structures.
  • To provide a more efficient and accurate tool for analyzing gene expression data.

Main Methods:

  • Developed a proximal gradient algorithm for differential network (ProGAdNet) inference.
  • Jointly infers two gene networks from gene expression data under distinct conditions.
  • Utilized computer simulations to evaluate performance against existing algorithms.

Main Results:

  • ProGAdNet demonstrated superior inference accuracy and significantly faster computation compared to existing methods.
  • Analysis of TCGA breast and kidney cancer data identified key genes and enriched gene sets related to cancer.
  • The identified gene sets were highly informative regarding cancer disease status.

Conclusions:

  • ProGAdNet offers a valuable and superior tool for detecting changes in gene networks.
  • The algorithm aids in discovering condition-specific gene-gene interactions.
  • This facilitates a deeper understanding of the molecular basis of diseases like cancer.