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

Artificial Intelligence in Medicine
Konstantinos TheofilatosSeferina Mavroudi

Abstract

Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datase...Continue Reading

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Citations

Aug 11, 2016·Artificial Intelligence in Medicine·Christos DimitrakopoulosSeferina Mavroudi
Jul 24, 2020·Journal of Bioinformatics and Computational Biology·Heng YaoTianying Liu
Apr 2, 2019·Annals of Vascular Diseases·Ali Kordzadeh, Shabnam Sadeghi Esfahlani
Feb 6, 2019·BMC Bioinformatics·Rama Kaalia, Jagath C Rajapakse
Sep 29, 2020·World Journal of Gastroenterology : WJG·Ke-Wei Wang, Ming Dong
Oct 7, 2015·Frontiers in Genetics·Konstantinos A TheofilatosSeferina Mavroudi
May 23, 2021·Cancer Cell International·Muhammad Javed IqbalJavad Sharifi-Rad

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