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Volumn 13, Issue 1, 2012, Pages

Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions

Author keywords

[No Author keywords available]

Indexed keywords

BIO-MARKER DISCOVERY; BREAST CANCER; CLASSIFICATION ACCURACY; CLINICAL PROGNOSIS; ELASTIC NET; GENE SELECTION; GENE SIGNATURES; INTERPRETABILITY; MEDICAL DOCTORS; NETWORK-BASED; PERSONALIZED MEDICINES; PREDICTION ACCURACY; PREDICTION PERFORMANCE; PRIOR KNOWLEDGE; PROTEIN INTERACTION; PROTEIN-PROTEIN INTERACTIONS; RECURSIVE FEATURE ELIMINATION; REPRODUCIBILITIES; THERAPEUTIC STRATEGY; THREE CATEGORIES;

EID: 84862181484     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-13-69     Document Type: Article
Times cited : (47)

References (52)
  • 1
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • 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.
    • (1996) J Roy Stat Soc B Met , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 2
    • 0037076272 scopus 로고    scopus 로고
    • Diagnosis of multiple cancer types by shrunken centroids of gene expression
    • 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.
    • (2002) Proc Natl Acad Sci U S A , vol.99 , Issue.10 , pp. 6567-6572
    • Tibshirani, R.1    Hastie, T.2    Narasimhan, B.3    Chu, G.4
  • 3
    • 0036161259 scopus 로고    scopus 로고
    • Gene Selection for Cancer Classification using Support Vector Machines
    • 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.
    • (2002) Mach. Learn , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 4
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • Breiman L. Random Forests. Mach Learn 2001, 45:5-32. http://dx.doi.org/10.1023/A:1010933404324.
    • (2001) Mach Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 6
    • 3543109140 scopus 로고    scopus 로고
    • A Feature Selection Newton Method for Support Vector Machine Classification
    • Fung G, Mangasarian O. A Feature Selection Newton Method for Support Vector Machine Classification. Comput Optim Appl 2004, 28:185-202. http://dx.doi.org/10.1023/B:COAP.0000026884.66338.df.
    • (2004) Comput Optim Appl , vol.28 , pp. 185-202
    • Fung, G.1    Mangasarian, O.2
  • 7
    • 30344438839 scopus 로고    scopus 로고
    • Gene selection using support vector machines with non-convex penalty
    • 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.
    • (2006) Bioinformatics , vol.22 , pp. 88-95
    • Zhang, H.H.1    Ahn, J.2    Lin, X.3    Park, C.4
  • 8
    • 38849091390 scopus 로고    scopus 로고
    • Hybrid huberized support vector machines for microarray classification and gene selection
    • 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.
    • (2008) Bioinformatics , vol.24 , Issue.3 , pp. 412-419
    • Wang, L.1    Zhu, J.2    Zou, H.3
  • 9
    • 13444282534 scopus 로고    scopus 로고
    • Outcome signature genes in breast cancer: is there a unique set?
    • 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.
    • (2005) Bioinformatics , vol.21 , Issue.2 , pp. 171-178
    • Ein-Dor, L.1    Kela, I.2    Getz, G.3    Givol, D.4    Domany, E.5
  • 10
    • 79952687473 scopus 로고    scopus 로고
    • Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?
    • 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.
    • (2011) PLoS One , vol.6 , Issue.3
    • Drier, Y.1    Domany, E.2
  • 11
    • 35348891430 scopus 로고    scopus 로고
    • Network-based classification of breast cancer metastasis
    • 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.
    • (2007) Mol Syst Biol , vol.3 , pp. 140
    • Chuang, H.Y.1    Lee, E.2    Liu, Y.T.3    Lee, D.4    Ideker, T.5
  • 13
    • 57149092133 scopus 로고    scopus 로고
    • Inferring pathway activity toward precise disease classification
    • 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.
    • (2008) PLoS Comput Biol , vol.4 , Issue.11
    • Lee, E.1    Chuang, H.Y.2    Kim, J.W.3    Ideker, T.4    Lee, D.5
  • 14
    • 61449157892 scopus 로고    scopus 로고
    • Incorporating pathway information into boosting estimation of high-dimensional risk prediction models
    • 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.
    • (2009) BMC Bioinformatics , vol.10 , pp. 18
    • Binder, H.1    Schumacher, M.2
  • 15
    • 60849121073 scopus 로고    scopus 로고
    • Network-based support vector machine for classification of microarray samples
    • 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.
    • (2009) BMC Bioinformatics , vol.10 , Issue.SUPPL. 1
    • Zhu, Y.1    Shen, X.2    Pan, W.3
  • 17
    • 77955886691 scopus 로고    scopus 로고
    • Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
    • 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.
    • (2010) Bioinformatics , vol.26 , Issue.17 , pp. 2136-2144
    • Johannes, M.1    Brase, J.C.2    Fröhlich, H.3    Gade, S.4    Gehrmann, M.5    Fälth, M.6    Sültmann, H.7    Beissbarth, T.8
  • 18
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • 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.
    • (2003) J. Mach. Learn. Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 19
    • 0028468293 scopus 로고
    • Using mutual information for selecting features in supervised neural net learning
    • 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.
    • (1994) IEEE Trans Neural Netw , vol.5 , Issue.4 , pp. 537-550
    • Battiti, R.1
  • 20
    • 0035942271 scopus 로고    scopus 로고
    • Significance analysis of microarrays applied to the ionizing radiation response
    • 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.
    • (2001) Proc Natl Acad Sci U S A , vol.98 , Issue.9 , pp. 5116-5121
    • Tusher, V.G.1    Tibshirani, R.2    Chu, G.3
  • 21
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: a practical and powerful approach to multiple testing
    • 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.
    • (1995) J Ros Stat Soc B Met , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 23
    • 70449408962 scopus 로고    scopus 로고
    • Graph ranking for exploratory gene data analysis
    • 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.
    • (2009) BMC Bioinformatics , vol.10 , Issue.SUPPL. 11
    • Gao, C.1    Dang, X.2    Chen, Y.3    Wilkins, D.4
  • 24
    • 79955757477 scopus 로고    scopus 로고
    • PathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery
    • 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.
    • (2011) Bioinformatics , vol.27 , Issue.10 , pp. 1442-1443
    • Johannes, M.1    Fröhlich, H.2    Sültmann, H.3    Beissbarth, T.4
  • 25
    • 27644503675 scopus 로고    scopus 로고
    • GeneRank: using search engine technology for the analysis of microarray experiments
    • 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.
    • (2005) BMC Bioinformatics , vol.6 , pp. 233
    • Morrison, J.L.1    Breitling, R.2    Higham, D.J.3    Gilbert, D.R.4
  • 26
    • 0036161011 scopus 로고    scopus 로고
    • Choosing Multiple Parameters for Support Vector Machines
    • Chapelle O, Vapnik V, Bousquet O, Mukherjee S. Choosing Multiple Parameters for Support Vector Machines. Mach Learn 2002, 46:131-159. http://dx.doi.org/10.1023/A:1012450327387.
    • (2002) Mach Learn , vol.46 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.2    Bousquet, O.3    Mukherjee, S.4
  • 27
    • 67649214465 scopus 로고    scopus 로고
    • PenalizedSVM: a R-package for feature selection SVM classification
    • 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.
    • (2009) Bioinformatics , vol.25 , Issue.13 , pp. 1711-1712
    • Becker, N.1    Werft, W.2    Toedt, G.3    Lichter, P.4    Benner, A.5
  • 28
    • 33750124478 scopus 로고    scopus 로고
    • Efficient Parameter Selection for Support Vector Machines in Classification and Regression via Model-Based Global Optimization
    • 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.
    • (2005) In Proc. Int. Joint Conf. Neural Networks , pp. 1431-1438
    • Fröhlich, H.1    Zell, A.2
  • 29
    • 33646023117 scopus 로고    scopus 로고
    • An introduction to ROC analysis
    • Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006, 27(8):861-874. http://www.sciencedirect.com/science/article/pii/S0167865505 00303X.
    • (2006) Pattern Recognition Letters , vol.27 , Issue.8 , pp. 861-874
    • Fawcett, T.1
  • 30
    • 27544491192 scopus 로고    scopus 로고
    • ROCR: visualizing classifier performance in R
    • 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.
    • (2005) Bioinformatics , vol.21 , Issue.20 , pp. 3940-3941
    • Sing, T.1    Sander, O.2    Beerenwinkel, N.3    Lengauer, T.4
  • 32
    • 0028931857 scopus 로고
    • Multiple significance tests: the Bonferroni method
    • Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ 1995, 310(6973):170.
    • (1995) BMJ , vol.310 , Issue.6973 , pp. 170
    • Bland, J.M.1    Altman, D.G.2
  • 33
    • 0035733108 scopus 로고    scopus 로고
    • The control of the false discovery rate in multiple testing under dependency
    • Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 2000, 29:1165-1188.
    • (2000) Annals of Statistics , vol.29 , pp. 1165-1188
    • Benjamini, Y.1    Yekutieli, D.2
  • 41
    • 33645823677 scopus 로고    scopus 로고
    • A new summarization method for Affymetrix probe level data
    • 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.
    • (2006) Bioinformatics , vol.22 , Issue.8 , pp. 943-949
    • Hochreiter, S.1    Clevert, D.A.2    Obermayer, K.3
  • 44
    • 65649116900 scopus 로고    scopus 로고
    • KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor
    • 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.
    • (2009) Bioinformatics , vol.25 , Issue.11 , pp. 1470-1471
    • Zhang, J.D.1    Wiemann, S.2
  • 45
    • 84865975985 scopus 로고    scopus 로고
    • Affymetrix Human Genome U133 Set annotation data (chip hgu133a) assembled using data from public repositories
    • 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.
    • (2009) Bioconductor version , vol.2 , Issue.2 , pp. 12
    • Carlson, M.1    Falcon, S.2    Pages, H.3    Li, N.4
  • 46
    • 0141757460 scopus 로고    scopus 로고
    • MAPK pathways in radiation responses
    • 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.
    • (2003) Oncogene , vol.22 , Issue.37 , pp. 5885-5896
    • Dent, P.1    Yacoub, A.2    Fisher, P.B.3    Hagan, M.P.4    Grant, S.5
  • 47
    • 0034600849 scopus 로고    scopus 로고
    • The ErbB signaling network: receptor heterodimerization in development and cancer
    • 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.
    • (2000) EMBO J , vol.19 , Issue.13 , pp. 3159-3167
    • Olayioye, M.A.1    Neve, R.M.2    Lane, H.A.3    Hynes, N.E.4
  • 48
    • 0032792155 scopus 로고    scopus 로고
    • The cadherin-catenin system: implications for growth and differentiation of endocrine tissues
    • 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.
    • (1999) Endocr Rev , vol.20 , Issue.2 , pp. 207-239
    • Pötter, E.1    Bergwitz, C.2    Brabant, G.3
  • 49
    • 0034494961 scopus 로고    scopus 로고
    • Focal adhesions: structure and dynamics
    • Petit V, Thiery JP. Focal adhesions: structure and dynamics. Biol Cell 2000, 92(7):477-494.
    • (2000) Biol Cell , vol.92 , Issue.7 , pp. 477-494
    • Petit, V.1    Thiery, J.P.2
  • 50
    • 77952961276 scopus 로고    scopus 로고
    • Network properties of human disease genes with pleiotropic effects
    • 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.
    • (2010) BMC Syst Biol , vol.4 , pp. 78
    • Chavali, S.1    Barrenas, F.2    Kanduri, K.3    Benson, M.4
  • 51
    • 83755163963 scopus 로고    scopus 로고
    • The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures
    • 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.
    • (2011) PLoS One , vol.6 , Issue.12 , pp. 28210
    • Haury, A.C.1    Gestraud, P.2    Vert, J.P.3
  • 52
    • 79955683050 scopus 로고    scopus 로고
    • Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data
    • 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.
    • (2011) , vol.12 , pp. 138
    • Becker, N.1    Toedt, G.2    Lichter, P.3    Benner, A.4


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