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Open Access Highly Accessed Research article

A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays

Francesca Demichelis123, Paolo Magni4, Paolo Piergiorgi4, Mark A Rubin235* and Riccardo Bellazzi4

Author Affiliations

1 Bionformatics, SRA, ITC-irst & Dept. of Information and Communication Technology, University of Trento, Trento, Italy

2 Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA

3 Harvard Medical School, Boston, MA, USA

4 Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy

5 Dana Farber Harvard Cancer Center, Boston, MA, USA

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BMC Bioinformatics 2006, 7:514  doi:10.1186/1471-2105-7-514

Published: 24 November 2006

Abstract

Background

Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example of biological uncertainty which should be taken into account when molecular markers are used in decision making. Tissue Microarray (TMA) experiments allow for large scale profiling of tissue biopsies, investigating protein patterns characterizing specific disease states. TMA studies deal with multiple sampling of the same patient, and therefore with multiple measurements of same protein target, to account for possible biological heterogeneity. The aim of this paper is to provide and validate a classification model taking into consideration the uncertainty associated with measuring replicate samples.

Results

We propose an extension of the well-known Naïve Bayes classifier, which accounts for biological heterogeneity in a probabilistic framework, relying on Bayesian hierarchical models. The model, which can be efficiently learned from the training dataset, exploits a closed-form of classification equation, thus providing no additional computational cost with respect to the standard Naïve Bayes classifier. We validated the approach on several simulated datasets comparing its performances with the Naïve Bayes classifier. Moreover, we demonstrated that explicitly dealing with heterogeneity can improve classification accuracy on a TMA prostate cancer dataset.

Conclusion

The proposed Hierarchical Naïve Bayes classifier can be conveniently applied in problems where within sample heterogeneity must be taken into account, such as TMA experiments and biological contexts where several measurements (replicates) are available for the same biological sample. The performance of the new approach is better than the standard Naïve Bayes model, in particular when the within sample heterogeneity is different in the different classes.