Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering
Highlights • EE-MC is a new unsupervised methodology for predicting protein complexes from weighted PPI graphs. • It is by design able to overcome intrinsic limitations of existing methodologies. • It outperformed existing methodologies increasing the separation metric by 10–20%. • 72.58% of the pre...
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| Veröffentlicht in: | Artificial intelligence in medicine 2015-03, Vol.63 (3), p.181-189 |
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| container_title | Artificial intelligence in medicine |
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| creator | Theofilatos, Konstantinos Pavlopoulou, Niki Papasavvas, Christoforos Likothanassis, Spiros Dimitrakopoulos, Christos Georgopoulos, Efstratios Moschopoulos, Charalampos Mavroudi, Seferina |
| description | Highlights • EE-MC is a new unsupervised methodology for predicting protein complexes from weighted PPI graphs. • It is by design able to overcome intrinsic limitations of existing methodologies. • It outperformed existing methodologies increasing the separation metric by 10–20%. • 72.58% of the predicted protein complexes in human are enriched for at least one GO function term. |
| doi_str_mv | 10.1016/j.artmed.2014.12.012 |
| format | Article |
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| subjects | Algorithms Cluster Analysis Clustering Computational Biology - methods Databases, Protein Evolutionary algorithms Evolutionary enhanced Markov clustering Functional characterization of proteins and protein complexes Genetic algorithms Human Humans Internal Medicine Large scale biological networks analysis Markov Chains Markov processes Mathematical analysis Methodology Other Protein complex prediction Protein Interaction Mapping - methods Protein Interaction Maps - physiology Proteins Saccharomyces cerevisiae State of the art Tuning Weighted protein–protein interaction networks |
| title | Predicting protein complexes from weighted protein–protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering |
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