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Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
Author(s) -
Clara Pizzuti,
Simona E. Rombo
Publication year - 2012
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2330163.2330191
Subject(s) - computer science , fitness function , protein interaction networks , yeast , topology (electrical circuits) , artificial intelligence , genetic algorithm , computational biology , machine learning , protein–protein interaction , biology , engineering , genetics , electrical engineering
The detection of groups of proteins sharing common biological features is an important research issue, intensively investigated in the last few years, because of the insights it can give in understanding cell behavior. In this paper we present an extensive experimental evaluation campaign aiming at exploring the capability of Genetic Algorithms (GAs) to find clusters in protein-protein interaction networks, when different topological-based fitness functions are employed. A complete experimentation on the yeast protein-protein interaction network, along with a comparative evaluation of the effectiveness in detecting true complexes on the yeast and human networks, reveals GAs as a feasible and competitive computational technique to cope with this problem.

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