« Previous
Next »
Artificial Intelligence in Medicine
Volume 44, Issue 3
, Pages 221-231
, November 2008
Using support vector regression to model the correlation between the clinical metastases time and gene expression profile for breast cancer
References
- Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355(6):560–569
- Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671–679
- A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999–2009
- . Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm. Clin Cancer Res. 2004;10(7):2272–2283
- . Good Old clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers. Eur J Cancer. 2004;40(12):1837–1841
- A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res. 2005;65(10):4059–4066
- . Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005;365(9458):488–492
- . Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics. 2003;19(17):2271–2282
- . Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003;19(Suppl 2):II138–II148
- . Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol. 2003;4(12):R83
- . Multiclass cancer classification using gene expression profiling and probabilistic neural networks. Pac Symp Biocomput. 2003;5–16
- . Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol. 2004;2(4):E108
- . A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2005;21(1):71–79
- Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics. 2005;21(11):2691–2697
- . Bayesian variable selection for the analysis of microarray data with censored outcomes. Bioinformatics. 2006;22(18):2262–2268
- Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–536
- Breast cancer prognosis by combinatorial analysis of gene expression data. Breast Cancer Res. 2006;8(4):R41
- . Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics. 2006;22(14):e184–e190
- . Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics. 2005;21(2):171–178
- . Molecular classification and molecular forecasting of breast cancer: ready for clinical application?. J Clin Oncol. 2005;23(29):7350–7360
- A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–2826
- . Data mining: practical machine learning tools and techniques. 2nd ed.. San Francisco: Morgan Kaufmann; 2005;
- . Wrappers for feature subset selection. Artif Intell. 1997;97(1–2):273–324
- Chang C-C, Lin C-J. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
- DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):P3
- Molecular characterization of the tumor microenvironment in breast cancer. Cancer Cell. 2004;6(1):17–32
- . Molecular signatures predict outcomes of breast cancer. N Engl J Med. 2006;355(6):615–617
PII: S0933-3657(08)00085-7
doi: 10.1016/j.artmed.2008.06.005
© 2008 Elsevier B.V. All rights reserved.
« Previous
Next »
Artificial Intelligence in Medicine
Volume 44, Issue 3
, Pages 221-231
, November 2008
