scholarly journals Application of soft regularized markov clustering for analyzing protein-protein interaction in sars-cov-2 and other related coronavirus

2021 ◽  
Vol 1722 ◽  
pp. 012012
Author(s):  
S A Pratiwi ◽  
A Bustamam ◽  
D Sarwinda
Keyword(s):  
Protein Interaction ◽  
Protein Protein Interaction ◽  
Markov Clustering

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.

Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering

2015 ◽  
Vol 63 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Konstantinos Theofilatos ◽  
Niki Pavlopoulou ◽  
Christoforos Papasavvas ◽  
Spiros Likothanassis ◽  
Christos Dimitrakopoulos ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Complexes ◽  
Interaction Graphs ◽  
Protein Protein Interaction ◽  
Markov Clustering
Sign in / Sign up

Export Citation Format

Share Document