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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Mining functional subgraphs from cancer protein-protein interaction networks
Ru Shen1, Nalin C W Goonesekere, Chittibabu Guda
1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, USA.
BMC Systems Biology
|January 4, 2013
Summary
We identified common network patterns in cancer protein-protein interaction networks using a novel algorithm. These frequent patterns offer insights into cancer biology and can guide experimental validation for a deeper understanding of the disease.
Area of Science:
- Bioinformatics
- Systems Biology
- Computational Biology
Background:
- Protein-protein interaction (PPI) networks are crucial for understanding protein functions.
- Analyzing cancer-associated PPI networks aids in deciphering complex disease biology.
- Identifying recurring patterns (network motifs) in PPI networks can reveal disease mechanisms.
Purpose of the Study:
- To develop a novel pattern-mining algorithm for detecting cancer-associated functional subgraphs.
- To discover frequent patterns across multiple cancer PPI networks.
- To identify functionally relevant and coherent subgraphs for further investigation.
Main Methods:
- Constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset.
- Developed a bottom-up pattern growth algorithm with canonical labeling and weighted adjacency matrices for efficient subgraph detection.
- Validated discovered patterns using Gene Ontology (GO) semantic similarity and literature-based evidence.
Main Results:
- Discovered frequent patterns common to all nine cancer PPI networks at various size levels.
- Validated patterns showed significantly higher GO semantic similarity scores compared to random subgraphs.
- Identified specific cancer-relevant subgraphs through literature review.
Conclusions:
- Frequent common patterns are present in cancer PPI networks and are discoverable via effective pattern-mining algorithms.
- The identified subgraphs are functionally relevant and coherent, providing valuable clues for cancer biology.
- This approach facilitates the identification of potential targets for experimental validation in cancer research.
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02:04Protein-protein Interfaces
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02:04Protein-Protein Interfaces
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