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Open Access Method

Classification methods for the development of genomic signatures from high-dimensional data

Hojin Moon1*, Hongshik Ahn2, Ralph L Kodell1, Chien-Ju Lin1, Songjoon Baek1 and James J Chen1

Author Affiliations

1 Division of Biometry and Risk Assessment, National Center for Toxicological Research, FDA, NCTR Road, Jefferson, AR 72079, USA

2 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA

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Genome Biology 2006, 7:R121  doi:10.1186/gb-2006-7-12-r121

Published: 20 December 2006

Abstract

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.