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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

Additional files

Additional file 1:

The marginal likelihood for the Hierarchical Naïve Bayes Model.

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Additional file 2:

Parameter values used to generate simulated data.

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Additional file 3:

Comparison of the proposed approach with other classification strategies on the TMA protein expression dataset.

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Additional file 4:

ROC Curves for the TMA protein expression dataset as calculated by running 100 times 10-fold cross validation (Table 4 in the paper).

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