An Algorithm to Assess the Reliability of Hierarchical Clusters in Gene Expression Data

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.

• Type: Journal
• Year: 2008
• Where: KES
• Authors: Roberto Avogadri, Matteo Brioschi, Francesca Ruffino, Fulvia Ferrazzi, Alessandro Beghini, Giorgio Valentini
• Keywords: Gene Expression Data, Information Technology, KES 2008, Large Number, Significant Clusters