  Methodology articlePrediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responsesRené Natowicz1 1 University of Paris – Est. ESIEE-Paris, Computer Sciences Department. Cité Descartes BP. 99, 93162 Noisy-le-Grand, France 2 Université Paris 12, Faculté de Médecine, Institut Mondor de Médecine Moléculaire (IFR10), Créteil, F-94000, France 3 Federal University of Minas Gerais, Brazil, Departamento de Engenharia Eletronica, Campus da UFMG (Pampulha), Av. Antonio Carlos, 6627, Belo Horizonte, MG 31270-901, Brazil 4 University of Texas M.D. Anderson Cancer Center, Department of Breast Medical Oncology, Unit 1354, PO Box 301439, Houston, Texas, USA 5 AP-HP, Hôpital Tenon, Department of Gynecology, 4 rue de la Chine, F-75020 Paris, France 6 Institut Gustave Roussy, Breast Cancer Unit, 39 rue Desmoulins, 94805 Villejuif, Cedex, France 7 UPMC Univ Paris 06, UPRES EA 4053, F-75005, Paris, France
BMC Bioinformatics 2008, 9:149doi:10.1186/1471-2105-9-149
Additional filesAdditional file 1: Figure – Expression levels of a PCR probe set, probe s = 213033_s_at of gene NFIB, for the 82 cases of the learning set. The data provided represent a PCR probe set. Format: PDF Size: 161KB Download file This file can be viewed with: Adobe Acrobat Reader Additional file 2: Figure – Expression levels of a NoPCR probe set, probe s = s = 203928_x_at of gene MAPT, for the 82 cases of the learning set. The data provided represent a NoPCR probe set. Format: PDF Size: 152KB Download file This file can be viewed with: Adobe Acrobat Reader Additional file 3: Figure – Discriminations of the two DLDA classifiers (30 probes with the highest valuation functions, and 30 probe sets showing the highest p-values (t-test)) in the independent test set 3. The data provided represent the performance metrics obtained in test set 3. Format: PDF Size: 29KB Download file This file can be viewed with: Adobe Acrobat Reader Additional file 4: Appendix – P-value of the predictors. The data provide the method used to calculate the p-values of the 27, 29, and 30 probe set predictors based on the null hypothesis of a predictor composed of random probe sets. Format: DOC Size: 54KB Download file This file can be viewed with: Microsoft Word Viewer Additional file 5: Table – Performance metrics of a multigene majority vote predictor (weighted valuation functions) for α ∈ {0, 0.1,...,1.0}. The data provided represent a family of valuation functions, vα(s), parameterized by the real number alpha, α ∈ [0, 1]. Format: DOC Size: 35KB Download file This file can be viewed with: Microsoft Word Viewer Additional file 6: Table – Ratios of pcr to nopcr predictions for the weighted valuation functions; P(α), N(α): total numbers of pcr and nopcr predictions of the top 30 probes in the ranking vα(s); R(α) = P(α)/N(α). The data provided represent the results obtained by parameterization of the valuation function by the real number alpha, α ∈ [0, 1]. Format: DOC Size: 35KB Download file This file can be viewed with: Microsoft Word Viewer Additional file 7: Figure – Sets top 30 probes for the weighted valuation functions. Underlined probes: mono-informative probes (either PCR or NoPCR probes). The data provided represent the top 30 probes obtained by parameterization of the valuation function by the real number alpha, α ∈ [0, 1]. Format: PDF Size: 987KB Download file This file can be viewed with: Adobe Acrobat Reader Additional file 8: Table – Patients characteristics. The data provided give the patient characteristics for the 4 cohorts of the study. Format: DOC Size: 52KB Download file This file can be viewed with: Microsoft Word Viewer |
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