Performance Evaluations of Active Subnetwork Search Methods in Protein-Protein Interaction Networks Protein-Protein Etkilesim Aglarinda Aktif Alt Ag Arama Yöntemlerinin Performans Degerlendirmeleri


GÜNER P. , GÜNGÖR B.

4th International Conference on Computer Science and Engineering, UBMK 2019, Samsun, Turkey, 11 - 15 September 2019, pp.650-655 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk.2019.8907137
  • City: Samsun
  • Country: Turkey
  • Page Numbers: pp.650-655
  • Keywords: protein-protein interaction network, active module, greedy approach, simulated annealing, genetic algorithm, prize collecting steiner forest, game theoretic approach

Abstract

© 2019 IEEE.Protein-protein interaction networks are mathematical representations of the physical contacts between proteins in the cell. A group of interconnected proteins in a protein-protein interaction network that contains most of the disease associated proteins and some interacting other proteins is called an active subnetwork. Active subnetwork search is important to understand mechanisms underlying diseases. Active subnetworks are used to discover disease related regulatory pathways, functional modules and to classify diseases. In the literature there are many methods to search for active subnetworks. The purpose of this study is to compare the performance of different subnetwork identification methods. By using the Rheumatoid Arthritis dataset, the performances of greedy approach, genetic algorithm, simulated annealing algorithm, prize collecting steiner forest and game theory based subnetwork search methods are compared.