Tibshirani R: Regression shrinkage and selection via the lasso. J Roy Stat Soc B Met. 1996, 58: 267-288. http://www.jstor.org/stable/2346178,
Google Scholar
Tibshirani R, Hastie T, Narasimhan B, Chu G: Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A. 2002, 99 (10): 6567-6572. http://dx.doi.org/10.1073/pnas.082099299,
Article
PubMed Central
CAS
PubMed
Google Scholar
Guyon I, Weston J, Barnhill S, Vapnik V: Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 2002, 46: 389-422. http://dx.doi.org/10.1023/A:1012487302797,
Article
Google Scholar
Breiman L: Random Forests. Mach Learn. 2001, 45: 5-32. 10.1023/A:1010933404324. http://dx.doi.org/10.1023/A:1010933404324,
Article
Google Scholar
Vapnik V: The nature of statistical learning theory. 2000, Springer, 2
Book
Google Scholar
Fung G, Mangasarian O: A Feature Selection Newton Method for Support Vector Machine Classification. Comput Optim Appl. 2004, 28: 185-202. 10.1023/B:COAP.0000026884.66338.df. http://dx.doi.org/10.1023/B:COAP.0000026884.66338.df,
Article
Google Scholar
Zhang HH, Ahn J, Lin X, Park C: Gene selection using support vector machines with non-convex penalty. Bioinformatics. 2006, 22: 88-95. http://dx.doi.org/10.1093/bioinformatics/bti736,
Article
PubMed
Google Scholar
Wang L, Zhu J, Zou H: Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics. 2008, 24 (3): 412-419. http://dx.doi.org/10.1093/bioinformatics/btm579,
Article
CAS
PubMed
Google Scholar
Ein-Dor L, Kela I, Getz G, Givol D, Domany E: Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics. 2005, 21 (2): 171-178. http://dx.doi.org/10.1093/bioinformatics/bth469,
Article
CAS
PubMed
Google Scholar
Drier Y, Domany E: Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?. PLoS One. 2011, 6 (3): e17795- http://dx.doi.org/10.1371/journal.pone.0017795,
Article
PubMed Central
CAS
PubMed
Google Scholar
Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007, 3: 140- http://dx.doi.org/10.1038/msb4100180,
Article
PubMed Central
PubMed
Google Scholar
Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert JP: Classification of microarray data using gene networks. BMC Bioinformatics. 2007, 8: 35- http://dx.doi.org/10.1186/1471-2105-8-35,
Article
PubMed Central
PubMed
Google Scholar
Lee E, Chuang HY, Kim JW, Ideker T, Lee D: Inferring pathway activity toward precise disease classification. PLoS Comput Biol. 2008, 4 (11): e1000217- http://dx.doi.org/10.1371/journal.pcbi.1000217,
Article
PubMed Central
PubMed
Google Scholar
Binder H, Schumacher M: Incorporating pathway information into boosting estimation of high-dimensional risk prediction models. BMC Bioinformatics. 2009, 10: 18- http://dx.doi.org/10.1186/1471-2105-10-18,
Article
PubMed Central
PubMed
Google Scholar
Zhu Y, Shen X, Pan W: Network-based support vector machine for classification of microarray samples. BMC Bioinformatics. 2009, 10 (Suppl 1): S21- http://dx.doi.org/10.1186/1471-2105-10-S1-S21,
Article
PubMed Central
PubMed
Google Scholar
Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009, 27 (2): 199-204. http://dx.doi.org/10.1038/nbt.1522,
Article
CAS
PubMed
Google Scholar
Johannes M, Brase JC, Fröhlich H, Gade S, Gehrmann M, Fälth M, Sültmann H, Beissbarth T: Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics. 2010, 26 (17): 2136-2144. http://dx.doi.org/10.1093/bioinformatics/btq345,
Article
CAS
PubMed
Google Scholar
Guyon I, Elisseeff A: An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3: 1157-1182. http://portal.acm.org/citation.cfm?id=944919.944968,
Google Scholar
Battiti R: Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw. 1994, 5 (4): 537-550. http://dx.doi.org/10.1109/72.298224,
Article
CAS
PubMed
Google Scholar
Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98 (9): 5116-5121. http://dx.doi.org/10.1073/pnas.091062498,
Article
PubMed Central
CAS
PubMed
Google Scholar
Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Ros Stat Soc B Met. 1995, 57: 289-300. http://www.jstor.org/stable/2346101,
Google Scholar
Guo Z, Zhang T, Li X, Wang Q, Xu J, Yu H, Zhu J, Wang H, Wang C, Topol EJ, Wang Q, Rao S: Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics. 2005, 6: 58- http://dx.doi.org/10.1186/1471-2105-6-58,
Article
PubMed Central
PubMed
Google Scholar
Gao C, Dang X, Chen Y, Wilkins D: Graph ranking for exploratory gene data analysis. BMC Bioinformatics. 2009, 10 (Suppl 11): S19- http://dx.doi.org/10.1186/1471-2105-10-S11-S19,
Article
PubMed Central
PubMed
Google Scholar
Johannes M, Fröhlich H, Sültmann H, Beissbarth T: pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery. Bioinformatics. 2011, 27 (10): 1442-1443. http://dx.doi.org/10.1093/bioinformatics/btr157,
Article
CAS
PubMed
Google Scholar
Morrison JL, Breitling R, Higham DJ, Gilbert DR: GeneRank: using search engine technology for the analysis of microarray experiments. BMC Bioinformatics. 2005, 6: 233- http://dx.doi.org/10.1186/1471-2105-6-233,
Article
PubMed Central
PubMed
Google Scholar
Chapelle O, Vapnik V, Bousquet O, Mukherjee S: Choosing Multiple Parameters for Support Vector Machines. Mach Learn. 2002, 46: 131-159. 10.1023/A:1012450327387. http://dx.doi.org/10.1023/A:1012450327387,
Article
Google Scholar
Becker N, Werft W, Toedt G, Lichter P, Benner A: penalizedSVM: a R-package for feature selection SVM classification. Bioinformatics. 2009, 25 (13): 1711-1712. http://dx.doi.org/10.1093/bioinformatics/btp286,
Article
CAS
PubMed
Google Scholar
Fröhlich H, Zell A: Efficient Parameter Selection for Support Vector Machines in Classification and Regression via Model-Based Global Optimization. In Proc. Int. Joint Conf. Neural Networks. 2005, 1431-1438.
Google Scholar
Fawcett T: An introduction to ROC analysis. Pattern Recognition Letters. 2006, 27 (8): 861-874. http://www.sciencedirect.com/science/article/pii/S016786550500303X,
Article
Google Scholar
Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics. 2005, 21 (20): 3940-3941. http://dx.doi.org/10.1093/bioinformatics/bti623,
Article
CAS
PubMed
Google Scholar
Osborne JD, Flatow J, Holko M, Lin SM, Kibbe WA, Zhu LJ, Danila MI, Feng G, Chisholm RL: Annotating the human genome with disease ontology. BMC Genomics. 2009, 10 (Suppl 1): S6- http://dx.doi.org/10.1186/1471-2164-10-S1-S6,
Article
PubMed Central
PubMed
Google Scholar
Bland JM, Altman DG: Multiple significance tests: the Bonferroni method. BMJ. 1995, 310 (6973): 170-
Article
PubMed Central
CAS
PubMed
Google Scholar
Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Annals of Statistics. 2000, 29: 1165-1188.
Google Scholar
Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005, 365 (9460): 671-679. http://dx.doi.org/10.1016/S0140-6736(05)17947-1,
Article
CAS
PubMed
Google Scholar
Pawitan Y, Bjöhle J, Amler L, Borg AL, Egyhazi S, Hall P, Han X, Holmberg L, Huang F, Klaar S, Liu ET, Miller L, Nordgren H, Ploner A, Sandelin K, Shaw PM, Smeds J, Skoog L, Wedrén S, Bergh J: Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res. 2005, 7 (6): R953-R964. http://dx.doi.org/10.1186/bcr1325,
Article
PubMed Central
CAS
PubMed
Google Scholar
Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Van de Vijver MJ, Bergh J, Piccart M, Delorenzi M: Gene Expression Profiling in Breast Cancer: Understanding the Molecular Basis of Histologic Grade To Improve Prognosis. J Natl Cancer Inst. 2006, 98 (4): 262-272. 262. http://jnci.oxfordjournals.org/content/98/4/262.abstract,
Article
CAS
PubMed
Google Scholar
Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, Lindahl T, Pawitan Y, Hall P, Nordgren H, Wong JEL, Liu ET, Bergh J, Kuznetsov VA, Miller LD: Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 2006, 66 (21): 10292-10301. http://dx.doi.org/10.1158/0008-5472.CAN-05-4414,
Article
CAS
PubMed
Google Scholar
Desmedt C, Piette F, Loi S, Wang Y, Lallemand F, Haibe-Kains B, Viale G, Delorenzi M, Zhang Y, D’Assignies D’Assignies D’Assignies D’Assignies D’Assignies MS, Bergh J, Lidereau R, Ellis P, Harris AL, Klijn JGM, Foekens JA, Cardoso F, Piccart MJ, Buyse M, Sotiriou C, Consortium TRANSBIG: Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res. 2007, 13 (11): 3207-3214.
Article
CAS
PubMed
Google Scholar
Schmidt M, Böhm D, von Törne C, Steiner E, Puhl A, Pilch H, Lehr HA, Hengstler JG, Kölbl H, Gehrmann M: The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res. 2008, 68 (13): 5405-5413. http://dx.doi.org/10.1158/0008-5472.CAN-07-5206,
Article
CAS
PubMed
Google Scholar
Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res. 2011, 39 (Database issue): D1005-D1010. http://dx.doi.org/10.1093/nar/gkq1184,
Article
PubMed Central
CAS
PubMed
Google Scholar
Hochreiter S, Clevert DA, Obermayer K: A new summarization method for Affymetrix probe level data. Bioinformatics. 2006, 22 (8): 943-949. http://dx.doi.org/10.1093/bioinformatics/btl033,
Article
CAS
PubMed
Google Scholar
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36: D480-D484. http://dx.doi.org/10.1093/nar/gkm882,
Article
PubMed Central
CAS
PubMed
Google Scholar
Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C: Pathway Commons, a web resource for biological pathway data. Nucleic Acids Res. 2011, 39 (Database issue): D685-D690. http://dx.doi.org/10.1093/nar/gkq1039,
Article
PubMed Central
CAS
PubMed
Google Scholar
Zhang JD, Wiemann S: KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor. Bioinformatics. 2009, 25 (11): 1470-1471. http://dx.doi.org/10.1093/bioinformatics/btp167,
Article
PubMed Central
CAS
PubMed
Google Scholar
Carlson M, Falcon S, Pages H, Li N: Affymetrix Human Genome U133 Set annotation data (chip hgu133a) assembled using data from public repositories. Bioconductor version. 2009, 2 (2): 12-
Google Scholar
Dent P, Yacoub A, Fisher PB, Hagan MP, Grant S: MAPK pathways in radiation responses. Oncogene. 2003, 22 (37): 5885-5896. http://dx.doi.org/10.1038/sj.onc.1206701,
Article
CAS
PubMed
Google Scholar
Olayioye MA, Neve RM, Lane HA, Hynes NE: The ErbB signaling network: receptor heterodimerization in development and cancer. EMBO J. 2000, 19 (13): 3159-3167. http://dx.doi.org/10.1093/emboj/19.13.3159,
Article
PubMed Central
CAS
PubMed
Google Scholar
Pötter E, Bergwitz C, Brabant G: The cadherin-catenin system: implications for growth and differentiation of endocrine tissues. Endocr Rev. 1999, 20 (2): 207-239.
Article
PubMed
Google Scholar
Petit V, Thiery JP: Focal adhesions: structure and dynamics. Biol Cell. 2000, 92 (7): 477-494.
Article
CAS
PubMed
Google Scholar
Chavali S, Barrenas F, Kanduri K, Benson M: Network properties of human disease genes with pleiotropic effects. BMC Syst Biol. 2010, 4: 78- http://dx.doi.org/10.1186/1752-0509-4-78,
Article
PubMed Central
PubMed
Google Scholar
Haury AC, Gestraud P, Vert JP: The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures. PLoS One. 2011, 6 (12): 28210-
Article
Google Scholar
Becker N, Toedt G, Lichter P, Benner A: Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data. . 2011, 12: 138- http://dx.doi.org/10.1186/1471-2105-12-138,