PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An algorithm to assess the reliability of hierarchical clusters in gene expression data
R. Avogadri, M. Brioschi, F. Ruffino, F. Ferrazzi, A. Beghini and Giorgio Valentini
In: Knowledge-Based Intelligent Information and Engineering Systems, 12th International Conference (2008) Springer, Lecture Notes in Computer Science , pp. 764-770.

Abstract

The validation of clusters discovered in bio-molecular data is a central issue in bioinformatics. Recently, stability-based methods have been successfully applied to the analysis of the reliability of clusterings characterized by a relatively low number of examples and clusters. Nevertheless, several problems in functional genomics are characterized by a very large number of examples and clusters. We present a stability-based algorithm to discover significant clusters in hierarchical clusterings with a large number of examples and clusters. Preliminary results on gene expression data of patients affected by Human Myeloid Leukemia, show how to apply the proposed method when thousands of gene clusters are involved.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:4315
Deposited By:Giorgio Valentini
Deposited On:13 March 2009