Impact of heuristics in clustering large biological networks.

Autor: Shafin MK; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Kabir KL; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Ridwan I; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Anannya TT; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Karim RS; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Hoque MM; Department of CSE, MIST, Mirpur Cantonment, Dhaka 1216, Bangladesh., Rahman MS; AℓEDA Group, Department of CSE, BUET, Dhaka 1215, Bangladesh. Electronic address: msrahman@cse.buet.ac.bd.
Jazyk: angličtina
Zdroj: Computational biology and chemistry [Comput Biol Chem] 2015 Dec; Vol. 59 Pt A, pp. 28-36. Date of Electronic Publication: 2015 Jul 26.
DOI: 10.1016/j.compbiolchem.2015.05.007
Abstrakt: Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising.
(Copyright © 2015 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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