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.
Discovering Significant Structures in Clustered Bio-molecular Data Through the Bernstein Inequality / A. Bertoni, G. Valentini (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE). - In: Knowledge-Based Intelligent Information and Engineering Systems : KES 2007 - WIRN 2007 / [a cura di] B. Apolloni, R. J. Howlett, L. Jain. - Berlin : Springer, 2007 Sep 14. - ISBN 9783540748281. - pp. 886-891 (( Intervento presentato al 11. convegno KES International Conference, KES 2007 XVII ItalianWorkshop on Neural Networks : September 12-14 tenutosi a Vietri sul mare nel 2007 [10.1007/978-3-540-74829-8_108].
Discovering Significant Structures in Clustered Bio-molecular Data Through the Bernstein Inequality
A. BertoniPrimo
;G. ValentiniUltimo
2007
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.Pubblicazioni consigliate
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