« Previous
Next »
Artificial Intelligence in Medicine
Volume 48, Issue 2
, Pages 75-82
, February 2010
A GMM-IG framework for selecting genes as expression panel biomarkers
References
- Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002;8:816–824
- Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001;98:13790–13795
- Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci USA. 2001;98:13784–13789
- A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med. 2007;356:11–20
- Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
- Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536
- . Marginal asymptotics for the “large P, small N” paradigm: With applications to microarray data. Ann Stat. 2007;35:1456–1486
- . Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics. 2005;21:171–178
- Data-driven analysis approach for biomarker discovery using molecular-profiling technologies. Biomarkers. 2005;10:153–172
- . Meta-analysis of gene expression data: a predictor-based approach. Bioinformatics. 2007;23:1599–1606
- . Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 2002;62:4427–4433
- Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinformatics. 2004;5:81
- . Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics. 2005;6:265
- . A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics. 2006;22:1745–1752
- . A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics. 2006;22:1608–1615
- . Feature selection for high-dimensional genomic microarray data. In: Brodley CE, Danyluk AP editor. Proceedings of the 18th international conference on machine learning. MA, USA: Morgan Kaufmann, Williamstown; 2001;p. 601–608
- . Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc. 1977;39:1–38
- . Elements of information theory. New York: Wiley; 1991;
- . A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics. 2008;24:374–382
- The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24:1151–1161
- Multiple-laboratory comparison of microarray platforms. Nat Methods. 2005;2:345–350
- . Support-vector networks. Mach Learn. 1995;20:273–297
- . Pattern classification and scene analysis. New York: Wiley; 1973;
- . Classification and regression trees. Boca Raton, Florida: Chapman & Hall/CRC; 1998;
- Integrative Array Analyzer: a software package for analysis of cross-platform and cross-species microarray data. Bioinformatics. 2006;22:1665–1667
- Expression profiling of primary non-small cell lung cancer for target identification. Oncogene. 2002;21:7749–7763
- Identification of PECAM-1 in solid tumor cells and its potential involvement in tumor cell adhesion to endothelium. J Biol Chem. 1993;268:22883–22894
- Blockade of RAGE-amphoterin signalling suppresses tumour growth and metastases. Nature. 2000;405:354–360
PII: S0933-3657(09)00097-9
doi: 10.1016/j.artmed.2009.07.006
© 2009 Elsevier B.V. All rights reserved.
« Previous
Next »
Artificial Intelligence in Medicine
Volume 48, Issue 2
, Pages 75-82
, February 2010
