Faculty Publications
Document Type
Article
Journal/Book/Conference Title Title
BMC Systems Biology
Volume
6
Issue
Suppl 3
First Page
S2
Abstract
Background: Protein-protein interaction (PPI) networks carry vital information about proteins’ functions. Analysis of PPI networks associated with specific disease systems including cancer helps us in the understanding of the complex biology of diseases. Specifically, identification of similar and frequently occurring patterns (network motifs) across PPI networks will provide useful clues to better understand the biology of the diseases.
Results: In this study, we developed a novel pattern-mining algorithm that detects cancer associated functional subgraphs occurring in multiple cancer PPI networks. We constructed nine cancer PPI networks using differentially expressed genes from the Oncomine dataset. From these networks we discovered frequent patterns that occur in all networks and at different size levels. Patterns are abstracted subgraphs with their nodes replaced by node cluster IDs. By using effective canonical labeling and adopting weighted adjacency matrices, we are able to perform graph isomorphism test in polynomial running time. We use a bottom-up pattern growth approach to search for patterns, which allows us to effectively reduce the search space as pattern sizes grow. Validation of the frequent common patterns using GO semantic similarity showed that the discovered subgraphs scored consistently higher than the randomly generated subgraphs at each size level. We further investigated the cancer relevance of a select set of subgraphs using literature-based evidences.
Conclusion: Frequent common patterns exist in cancer PPI networks, which can be found through effective pattern mining algorithms. We believe that this work would allow us to identify functionally relevant and coherent subgraphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex biology of cancer.
Department
Department of Chemistry and Biochemistry
Original Publication Date
2012
DOI of published version
10.1186/1752-0509-6-S3-S2
Repository
UNI ScholarWorks, University of Northern Iowa, Rod Library
Copyright
©2012 Ru Shen, Nalin C. W. Goonesekere, and Chittibabu Guda. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Date Digital
2012
Language
EN
File Format
application/pdf
Recommended Citation
Shen, Ru; Goonesekere, Nalin; and Guda, Chittibabu, "Mining functional subgraphs from cancer protein-protein interaction networks" (2012). Faculty Publications. 3.
https://scholarworks.uni.edu/che_facpub/3
Comments
First published in BMC Systems Biology, v.6 n.Suppl 3 (2016), p. S2, by BioMed Central Ltd. DOI: http://dx.doi.org/10.1186/1752-0509-6-S3-S2