PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Discovering Significant Structures in Clustered Bio-molecular Data Through the Bernstein Inequality
Alberto Bertoni and Giorgio Valentini
In: Knowledge-Based Intelligent Information and Engineering Systems Lecture Notes in Computer Science , vol.4924 (4924). (2007) Springer , Berlin, Germany , pp. 886-891. ISBN 978-3-540-74828-1

Abstract

Searching for structures in complex bio-molecular data is a central issue in several branches of bioinformatics. In particular, the reliability of clusters discovered by a given clustering algorithm have been recently assessed through methods based on the concept of stability with respect to random perturbations of the data. In this context, a major problem is to assess the confidence of the measures of reliability. We discuss a partially "distribution independent" method based on the classical Bernstein inequality to assess the statistical significance of the discovered clusterings. Experimental results with gene expression data show the effectiveness of the proposed approach.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3579
Deposited By:Giorgio Valentini
Deposited On:13 February 2008