IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs

Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs
View Sample PDF
Author(s): Charalampos Moschopoulos (Biomedical Research Foundation of the Academy of Athens, Greece & University of Patras, Greece), Grigorios Beligiannis (University of Western Greece, Greece), Spiridon Likothanassis (University of Patras, Greece)and Sophia Kossida (Biomedical Research Foundation of the Academy of Athens, Greece)
Copyright: 2013
Pages: 12
Source title: Bioinformatics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3604-0.ch043

Purchase

View Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs on the publisher's website for pricing and purchasing information.

Abstract

In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

Related Content

Linkon Chowdhury, Md Sarwar Kamal, Shamim H. Ripon, Sazia Parvin, Omar Khadeer Hussain, Amira Ashour, Bristy Roy Chowdhury. © 2024. 20 pages.
Mousomi Roy. © 2024. 21 pages.
Nassima Dif, Zakaria Elberrichi. © 2024. 20 pages.
Pyingkodi Maran, Shanthi S., Thenmozhi K., Hemalatha D., Nanthini K.. © 2024. 16 pages.
Mohamed Nadjib Boufenara, Mahmoud Boufaida, Mohamed Lamine Berkane. © 2024. 16 pages.
Meroua Daoudi, Souham Meshoul, Samia Boucherkha. © 2024. 25 pages.
Zhongyu Lu, Qiang Xu, Murad Al-Rajab, Lamogha Chiazor. © 2024. 56 pages.
Body Bottom