PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A stability-based algorithm to validate hierarchical clusters of genes
Roberto Avogadri, Matteo Brioschi, Fulvia Ferrazzi, Matteo Re, Alessandro Beghini and Giorgio Valentini
International Journal of Knowledge Engineering and Soft Data Paradigms Volume 1, Number 4, pp. 318-330, 2009.


Stability-based methods have been successfully applied in functional genomics to the analysis of the reliability of clusterings characterized by a relatively low number of examples and clusters. The application of these methods to the validation of gene clusters discovered in biomolecular data may lead to computational problems due to the large amount of possible clusters involved. To address this problem, we present a stability-based algorithm to discover significant clusters in hierarchical clusterings with a large number of examples and clusters. The reliability of clusters of genes discovered in gene expression data of patients affected by human myeloid leukaemia is analysed through the proposed algorithm, and their relationships with specific biological processes are tested by means of Gene Ontology-based functional enrichment methods.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6308
Deposited By:Giorgio Valentini
Deposited On:08 March 2010