Abstract
A novel technique to search for functional modules in a protein-protein interaction network is presented. The network is represented by the adjacency matrix associated with the undirected graph modelling it. The algorithm introduces the concept of quality of a sub-matrix of the adjacency matrix, and applies a greedy search technique for finding local optimal solutions made of dense sub-matrices containing the maximum number of ones. An initial random solution, constituted by a single protein, is evolved to search for a locally optimal solution by adding/removing connected proteins that best contribute to improve the quality function. Experimental evaluations carried out on Saccaromyces Cerevisiae proteins show that the algorithm is able to efficiently isolate groups of biologically meaningful proteins corresponding to the most compact sets of interactions.
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Pizzuti, C., Rombo, S.E. (2007). PINCoC: A Co-clustering Based Approach to Analyze Protein-Protein Interaction Networks . In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_82
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DOI: https://doi.org/10.1007/978-3-540-77226-2_82
Publisher Name: Springer, Berlin, Heidelberg
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