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RASMA: a reverse search algorithm for mining maximal frequent subgraphs.

Saeed Salem1, Mohammed Alokshiya2, Mohammad Al Hasan3

  • 1North Dakota State University, Fargo, ND, 58102, USA. saeed.salem@ndsu.edu.

Biodata Mining
|March 17, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces RASMA, a novel reverse search algorithm for efficiently identifying frequent and maximal frequent subgraphs in gene coexpression networks. RASMA aids in discovering significant biological modules and biomarkers for disease classification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying frequently occurring subnetworks (frequent subgraphs) within gene coexpression networks is crucial for understanding gene interactions.
  • Maximal frequent subgraphs represent a concise set of significant network patterns.
  • These subnetworks can reveal biological modules and signatures for gene expression and disease classification.

Purpose of the Study:

  • To develop an efficient algorithm for mining frequent and maximal frequent subgraphs from collections of graphs.
  • To enhance the discovery of biologically relevant network motifs and modules in gene coexpression data.

Main Methods:

  • Proposed a reverse search algorithm named RASMA (Reverse Search Maximal Subgraphs Algorithm).
  • Introduced a connected subgraph enumerator employing a reverse-search strategy.
Keywords:
Biological networksFrequent subgraphsMaximal subgraphsReverse searchSubgraph enumeration

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  • Implemented pruning strategies to optimize runtime performance for large datasets.

    • Main Results:

    • RASMA efficiently enumerates all maximal frequent subgraphs.
    • The algorithm demonstrates substantial improvements in runtime performance due to effective pruning.
    • Experimental results confirm efficient mining of biologically relevant maximal frequent subgraphs on large-scale gene coexpression networks.

    Conclusions:

    • Extracting recurrent gene coexpression subnetworks facilitates the discovery of functional modules and subnetwork biomarkers.
    • The proposed RASMA algorithm effectively mines maximal frequent subnetworks.
    • Enrichment analysis shows that frequent subnetworks are highly associated with known biological ontologies.