ArticleBMC bioinformatics2008
Discovering multi-level structures in bio-molecular data through the Bernstein inequality.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Modern technologies and algorithms for scaffolding assembled genomes.PLoS computational biology · 2019Review
- Interpolation based consensus clustering for gene expression time series.BMC bioinformatics · 2015Article
- Hierarchical ensemble methods for protein function prediction.ISRN bioinformatics · 2014Review
- Dynamically weighted clustering with noise set.Bioinformatics (Oxford, England) · 2010Article
- caBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.BMC bioinformatics · 2008Article
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2 authors.
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Abstract
backgroundThe 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.
resultsTo 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 assuring a reliable application to a large range of bioinformatics problems. Results with synthetic and DNA microarray data show the effectiveness of the proposed method.
conclusionsThe Bernstein test, due to its loose assumptions, is more sensitive than the chi-square test to the detection of multiple structures simultaneously present in the data. Nevertheless it is less selective, that is subject to more false positives, but adding independence assumptions, a more selective variant of the Bernstein inequality-based test is also presented. The proposed methods can be applied to discover multiple structures and to assess their significance in different types of bio-molecular data.
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