scholarly journals Bounded list injective homomorphism for comparative analysis of protein–protein interaction graphs

2008 ◽  
Vol 6 (2) ◽  
pp. 178-191 ◽  
Author(s):  
Isabelle Fagnot ◽  
Gaëlle Lelandais ◽  
Stéphane Vialette
Keyword(s):  
Comparative Analysis ◽  
Protein Interaction ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Injective Homomorphism

scholarly journals Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

2011 ◽  
Vol 12 (1) ◽  
pp. 239 ◽  
Author(s):  
George D Kritikos ◽  
Charalampos Moschopoulos ◽  
Michalis Vazirgiannis ◽  
Sophia Kossida
Keyword(s):  
Noise Reduction ◽  
Protein Interaction ◽  
Weighting Scheme ◽  
Interaction Graphs ◽  
Protein Protein Interaction

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

2012 ◽  
Vol 1 (2) ◽  
pp. 35-47
Author(s):  
Charalampos Moschopoulos ◽  
Grigorios Beligiannis ◽  
Spiridon Likothanassis ◽  
Sophia Kossida
Keyword(s):  
Genetic Algorithm ◽  
Protein Interaction ◽  
Clustering Algorithm ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Successful Prediction ◽  
Markov Clustering

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.

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

2013 ◽  
pp. 805-816
Author(s):  
Charalampos Moschopoulos ◽  
Grigorios Beligiannis ◽  
Spiridon Likothanassis ◽  
Sophia Kossida
Keyword(s):  
Genetic Algorithm ◽  
Protein Interaction ◽  
Clustering Algorithm ◽  
Protein Complexes ◽  
Clustering Algorithms ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Markov Clustering ◽  
Complex Detection

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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