Research article
A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays
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* Corresponding author: Mark A Rubin marubin@partners.org
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
BMC Bioinformatics 2006, 7:514 doi:10.1186/1471-2105-7-514
Published: 24 November 2006Additional 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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