Discovering multi-level structures in bio-molecular data through the Bernstein inequality

Background: The unsupervised discovery of structures (i.e. clusterings) underlying data is a central issue in several branches of bioinformatics. Methods based on the concept of stability have been recently proposed to assess the reliability of a clustering procedure and to estimate the "optimal" number of clusters in bio-molecular data. A major problem with stability-based methods is the detection of multi-level structures (e.g. hierarchical functional classes of genes), and the assessment of their statistical significance. In this context, a chi-square based statistical test of hypothesis has been proposed; however, to assure the correctness of this technique some assumptions about the distribution of the data are needed. Results: To assess the statistical significance and to discover multi-level structures in bio-molecular data, a new method based on Bernstein's inequality is proposed. This approach makes no assumptions about the distribution of the data, thus assuri...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Alberto Bertoni, Giorgio Valentini
• Keywords: Bio-molecular Data, BMCBI 2008, Multi-level Structures, Statistical Significance