Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems.
The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis.
Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks.
Several types of computational methods have been developed and employed for the analysis of PPI networks.
Of these computational methods, graph comparison and module detection are the two most commonly used strategies.
This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods.
Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks.
American Psychological Association (APA)
Shen, Ru& Guda, Chittibabu. 2014. Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks. BioMed Research International،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-472452
Modern Language Association (MLA)
Shen, Ru& Guda, Chittibabu. Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks. BioMed Research International No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-472452
American Medical Association (AMA)
Shen, Ru& Guda, Chittibabu. Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-472452
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-472452