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