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Genome Biology 2006, 7:R121 commentreviewsreportsdepositedresearchrefereedresearchinteractionsinformation
 

Summary: Genome Biology 2006, 7:R121
commentreviewsreportsdepositedresearchrefereedresearchinteractionsinformation
Open Access2006Moonet al.Volume 7, Issue 12, Article R121Method
Classification methods for the development of genomic signatures
from high-dimensional data
Hojin Moon*, Hongshik Ahn, Ralph L Kodell*, Chien-Ju Lin*,
Songjoon Baek* and James J Chen*
Addresses: *Division of Biometry and Risk Assessment, National Center for Toxicological Research, FDA, NCTR Road, Jefferson, AR 72079,
USA. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
Correspondence: Hojin Moon. Email: hojin.moon@fda.hhs.gov
2006 Moon et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Methods for genomic signatures

Several classification algorithms for class prediction using high-dimensional biomedical data are presented and applied to data fromleukaemia and breast cancer patients


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/

  

Source: Ahn, Hongshik - Department of Applied Mathematics and Statistics, SUNY at Stony Brook

 

Collections: Materials Science