FMFinder: A Functional Module Detector for PPI Networks

Authors

  • M. Modi Computer Engineering Department, Marwadi Education Foundation, Rajkot, Gujarat
  • N. G. Jadeja Information Technology Department, Marwadi Education Foundation, Rajkot, Gujarat
  • K. Zala Marwadi Education Foundation, Rajkot, Gujarat
Volume: 7 | Issue: 5 | Pages: 2022-2025 | October 2017 | https://doi.org/10.48084/etasr.1347

Abstract

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

Downloads

Download data is not yet available.

References

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

Downloads

How to Cite

[1]
M. Modi, N. G. Jadeja, and K. Zala, “FMFinder: A Functional Module Detector for PPI Networks”, Eng. Technol. Appl. Sci. Res., vol. 7, no. 5, pp. 2022–2025, Oct. 2017.

Metrics

Abstract Views: 479
PDF Downloads: 214

Metrics Information
Bookmark and Share