Algorithms for clustering data
Algorithms for clustering data
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Functional topology in a network of protein interactions
Bioinformatics
PIVOT: Protein Interacions VisualizatiOn Tool
Bioinformatics
Protein complex prediction via cost-based clustering
Bioinformatics
Iterative Cluster Analysis of Protein Interaction Data
Bioinformatics
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Improving functional modularity in protein-protein interactions graphs using hub-induced subgraphs
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Experimental comparison of biclustering algorithms for PPI networks
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Anovel technique to search for functionalmodules 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 submatrices 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.