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Restricted Neighborhood Search Clustering Revisited: An Evolutionary Computation Perspective

  • Clara Pizzuti
  • Simona E. Rombo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

Abstract

Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of groups of proteins strictly related, can be useful to predict protein functions. Clustering techniques have been widely employed to detect significative biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccaromices cerevisiae show that the clusters obtained by the genetic approach are more accurate than those found by RNSC, though this method predicts more true complexes.

Keywords

Genetic Algorithm Cost Function Protein Interaction Network Predict Protein Function True Complex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Clara Pizzuti
    • 1
  • Simona E. Rombo
    • 2
  1. 1.Institute for High Performance Computing and Networking, National Research Council of ItalyCNR-ICARRendeItaly
  2. 2.Department of Mathematics, Computer Science SectionUniversitá degli Studi di PalermoPalermoItaly

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