Research article
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
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* Corresponding author: Lajos Pusztai lpusztai@mdanderson.org
1 Bioinformatics Core Facility, Swiss Institute of Bioinformatics, Génopode Building, Quartier Sorge, Lausanne CH-1015, Switzerland
2 Center for Devices and Radiological Health, US Food and Drug Administration, 10903 New Hampshire Ave WO62-3124, Silver Springs, MD 20993-0002, USA
3 Nuvera Biosciences, 400 West Cummings Park, Woburn, MA 01801, USA
4 GeneGo, Inc., 500 Renaissance Drive, St. Joseph, MI 49085, USA
5 Department of Systems Biology, Vavilov Institute for General Genetics, Russian Academy of Sciences, Gubkina str. 3 korp. 1, Moscow 119333, Russia
6 Department of Biostatistics, P.O. Box 301439, Houston, TX 77230-1439, USA
7 Department of Breast Medical Oncology, P.O. Box 301439, Houston, TX 77230-1439, USA
8 Swiss NCCR Molecular Oncology, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
9 National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
10 Department of Pathology of the University of Texas M. D. Anderson Cancer Center, P.O. Box 301439, Houston, TX 77230-1439, USA
Breast Cancer Research 2010, 12:R5 doi:10.1186/bcr2468
Published: 11 January 2010Additional files
Additional file 1:
Supplemental Table S1. Clinical data for all the patients in the training and validation sets.
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Additional file 2:
Supplemental Table S2. Quality control results.
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Additional file 3:
Supplemental Table S3. Pathways mapping for all endpoints.
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Additional file 4:
Supplemental methods. Pseudo-code description of the two-level external cross-validation scheme.
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Additional file 5:
Supplemental Table S4. Features (probesets) selected in the 120 models.
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Additional file 6:
Supplemental Table S5. Estimated and validation performance of all models.
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