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A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure

Rongquan Wang1,2, Caixia Wang3, Liyan Sun1,2

  • 1College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China.

BMC Genomics
|August 9, 2019
PubMed
Summary
This summary is machine-generated.

A new algorithm, SE-DMTG, enhances protein complex detection by integrating topological structure and Gene Ontology (GO) annotations. SE-DMTG improves accuracy and identifies complexes with varying densities, outperforming existing methods.

Keywords:
DensityGraph clustering algorithmsModularityProtein complexProtein-protein interaction networksfunctional properties

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein complex detection is crucial for understanding complex diseases and drug development.
  • Existing computational methods often rely solely on topological information, making them vulnerable to inaccuracies in protein-protein interaction networks (PPINs).
  • Current algorithms may fail to identify protein complexes exhibiting diverse densities and modularities.

Purpose of the Study:

  • To develop a novel algorithm for accurate protein complex detection in PPINs.
  • To address the limitations of existing methods by incorporating diverse data sources and advanced strategies.
  • To improve the identification of protein complexes with varied structural properties.

Main Methods:

  • Proposed a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations (SE-DMTG).
  • Constructed a weighted PPIN using common neighbors and GO annotations.
  • Implemented a new seed selection strategy and a fitness function to detect complexes with varying densities and modularities.

Main Results:

  • SE-DMTG demonstrated superior performance compared to thirteen state-of-the-art algorithms on multiple real datasets.
  • The algorithm achieved higher F-measure and Jaccard indices in yeast PPINs.
  • SE-DMTG showed excellent accuracy and matching ratios when applied to PPINs of other species.

Conclusions:

  • SE-DMTG significantly improves protein complex detection accuracy.
  • The algorithm effectively identifies protein complexes with diverse densities and modularities.
  • SE-DMTG shows broad applicability and robust performance across different species' PPINs.