FMFinder: A Functional Module Detector for PPI Networks
Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI) network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define the limits for module detection as well as clarify the construction of a PPI network. For understating the bio-mechanism of various living beings, a detailed study of FMFinder detection by clustering process is called for.
Keywords:functional modules, protein, PPI network, detection methods, inferring PPI network
J. Ji, A. Zhang, C. Liu, X. Quan, Z. Liu, “Survey: Functional Module Detection from Protein-Protein Interaction Networks”, IEEE Transaction on Knowledge and Data Engineering, Vol. 26, No. 2, pp. 261-273, 2014 DOI: https://doi.org/10.1109/TKDE.2012.225
M. Li, X. Wu, J. Wang, Y. Pan, “Towards the Identification of Protein Complexes and Functional Modules by Integrating PPI Network and Gene Expression Data”, BCM Bioinformatics, pp. 1-12, 2012 DOI: https://doi.org/10.1186/1471-2105-13-109
L. Shi, Y. R. Cho, A. Zhang, “Prediction of Protein Function from Connectivity of Protein Interaction Network”, International Journal of Computational Bioscience, Vol. 1, pp. 1-5, 2010 DOI: https://doi.org/10.2316/Journal.210.2010.1.210-1009
Q. Yu, G. H. Li, J. F. Huang, “MOfinder: A Novel Algorithm for Detecting Overlapping Modules from Protein-Protein Interaction Network”, Journal of Biomedicine and Biotechnology, Vol. 2012, pp. 1-10, 2012 DOI: https://doi.org/10.1155/2012/103702
] S. Zhang, H. W. Liu, X. M. Ning, X. S. Zhang, “A hybrid graph-theoretic method for mining overlapping functional modules in large sparse protein interaction networks”, International Journal of Data Mining and Bioinformatics, Vol. 3, No. 1, pp. 68–84, 2009 DOI: https://doi.org/10.1504/IJDMB.2009.023885
M. Wu, X. Li, C. K. Kwoh, S. K. Ng, “A core-attachment based method to detect protein complexes in PPI networks”, BMC Bioinformatics, Vol. 10, pp. 1-5, 2009 DOI: https://doi.org/10.1186/1471-2105-10-169
S. Zhang, R. S. Wang, X. S. Zhang, “Identification of overlapping community structure in complex networks using fuzzy c-means clustering”, Physica A, Vol. 374, No. 1, pp. 483– 4490, 2007 DOI: https://doi.org/10.1016/j.physa.2006.07.023
C. Wang, C. Ding, Q. Yang, S. R. Holbrook, “Consistent dissection of the protein interaction network by combining global and local metrics”, Genome Biology, Vol.8, No.12, pp. 1-10, 2007 DOI: https://doi.org/10.1186/gb-2007-8-12-r271
How to Cite
MetricsAbstract Views: 495
PDF Downloads: 261
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.