Including network knowledge into Cox regression models for biomarker signature discovery
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[1] Igor Jurisica,et al. Inferring the functions of longevity genes with modular subnetwork biomarkers of Caenorhabditis elegans aging , 2010, Genome Biology.
[2] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[3] Alex Arenas,et al. Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules , 2010, BMC Cancer.
[4] Georg Heinze,et al. Gene selection in microarray survival studies under possibly non-proportional hazards , 2010, Bioinform..
[5] Xiaodong Lin,et al. Gene expression Gene selection using support vector machines with non-convex penalty , 2005 .
[6] Yi Zhang,et al. Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer , 2007, BMC Cancer.
[7] Philippe Lambert. Modelling of non-linear growth curves on series of correlated count data measured at unequally spaced times: a full likelihood based approach , 1996 .
[8] Dragomir R. Radev,et al. Identifying gene-disease associations using centrality on a literature mined gene-interaction network , 2008, ISMB.
[9] D. Cox. Regression Models and Life-Tables , 1972 .
[10] Holger Fröhlich,et al. Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics , 2013, PloS one.
[11] L. Holmberg,et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts , 2005, Breast Cancer Research.
[12] M. Schumacher,et al. Consistent Estimation of the Expected Brier Score in General Survival Models with Right‐Censored Event Times , 2006, Biometrical journal. Biometrische Zeitschrift.
[13] Stefan Wiemann,et al. KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor , 2009, Bioinform..
[14] David Warde-Farley,et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.
[15] Holger Fröhlich,et al. Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions , 2012, BMC Bioinformatics.
[16] J. Bergh,et al. Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.
[17] A. Zell,et al. Efficient parameter selection for support vector machines in classification and regression via model-based global optimization , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[18] Samuel Granjeaud,et al. Prognosis of Breast Cancer and Gene Expression Profiling Using DNA Arrays , 2002, Annals of the New York Academy of Sciences.
[19] Desmond J. Higham,et al. GeneRank: Using search engine technology for the analysis of microarray experiments , 2005, BMC Bioinformatics.
[20] Mithat Gonen. Statistical aspects of gene signatures and molecular targets. , 2009 .
[21] Harald Binder,et al. Incorporating pathway information into boosting estimation of high-dimensional risk prediction models , 2009, BMC Bioinformatics.
[22] Salim A. Chowdhury,et al. Identification of Coordinately Dysregulated Subnetworks in Complex Phenotypes , 2010, Pacific Symposium on Biocomputing.
[23] Tobias Müller,et al. Bioinformatics Applications Note Systems Biology Bionet: an R-package for the Functional Analysis of Biological Networks , 2022 .
[24] Klaus Obermayer,et al. A new summarization method for affymetrix probe level data , 2006, Bioinform..
[25] Li Wang,et al. Hybrid huberized support vector machines for microarray classification , 2007, ICML '07.
[26] Jelle J. Goeman,et al. A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..
[27] Yoshihiro Yamanishi,et al. KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..
[28] Dennis B. Troup,et al. NCBI GEO: archive for functional genomics data sets—10 years on , 2010, Nucleic Acids Res..
[29] Lodewyk F. A. Wessels,et al. A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer , 2011, PloS one.
[30] Michel Lang,et al. Survival models with preclustered gene groups as covariates , 2011, BMC Bioinformatics.
[31] Holger Fröhlich,et al. Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients , 2010, Bioinform..
[32] Wei Pan,et al. Network-based support vector machine for classification of microarray samples , 2009, BMC Bioinformatics.
[33] O. Aalen,et al. Further results on the non-parametric linear regression model in survival analysis. , 1993, Statistics in medicine.
[34] Guanming Wu,et al. A network module-based method for identifying cancer prognostic signatures , 2012, Genome Biology.
[35] N. Breslow,et al. Analysis of Survival Data under the Proportional Hazards Model , 1975 .
[36] Trey Ideker,et al. Protein Networks as Logic Functions in Development and Cancer , 2011, PLoS Comput. Biol..
[37] H. Zou. The Adaptive Lasso and Its Oracle Properties , 2006 .
[38] Gary D. Bader,et al. Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..
[39] E Graf,et al. Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.
[40] Lee-Jen Wei,et al. The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. , 1992, Statistics in medicine.
[41] H. Fröhlich,et al. Network Based Consensus Gene Signatures for Biomarker Discovery in Breast Cancer , 2011, PloS one.
[42] H. Kölbl,et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. , 2008, Cancer research.
[43] Doheon Lee,et al. Inferring Pathway Activity toward Precise Disease Classification , 2008, PLoS Comput. Biol..
[44] Yi Pan,et al. Integration of breast cancer gene signatures based on graph centrality , 2011, BMC Systems Biology.
[45] W. Kibbe,et al. Annotating the human genome with Disease Ontology , 2009, BMC Genomics.
[46] Michalis E. Blazadonakis,et al. Integration of gene signatures using biological knowledge , 2011, Artif. Intell. Medicine.
[47] Axel Benner,et al. Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data , 2011, BMC Bioinformatics.
[48] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[49] J. Goeman. L1 Penalized Estimation in the Cox Proportional Hazards Model , 2009, Biometrical journal. Biometrische Zeitschrift.
[50] Mingguang Shi,et al. A Network-Based Gene Expression Signature Informs Prognosis and Treatment for Colorectal Cancer Patients , 2012, PloS one.
[51] Axel Benner,et al. High‐Dimensional Cox Models: The Choice of Penalty as Part of the Model Building Process , 2010, Biometrical journal. Biometrische Zeitschrift.
[52] Petter Holme,et al. Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies , 2009, PloS one.
[53] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[54] P. J. Verweij,et al. Cross-validation in survival analysis. , 1993, Statistics in medicine.
[55] Emmanuel Barillot,et al. Classification of microarray data using gene networks , 2007, BMC Bioinformatics.
[56] Z. Shao,et al. Integrated gene expression profile predicts prognosis of breast cancer patients , 2008, Breast Cancer Research and Treatment.
[57] Joshy George,et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. , 2006, Cancer research.
[58] J. Foekens,et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.
[59] T. Ideker,et al. Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.
[60] M. West,et al. Gene expression predictors of breast cancer outcomes , 2003, The Lancet.
[61] Qing Wang,et al. Towards precise classification of cancers based on robust gene functional expression profiles , 2005, BMC Bioinformatics.
[62] Martin Ester,et al. Optimally discriminative subnetwork markers predict response to chemotherapy , 2011, Bioinform..
[63] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[64] R. Tibshirani. The lasso method for variable selection in the Cox model. , 1997, Statistics in medicine.
[65] Michael Schroeder,et al. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes , 2012, PLoS Comput. Biol..
[66] Holger Fröhlich,et al. Review Biomarker Gene Signature Discovery Integrating Network Knowledge , 2012 .
[67] Rajeev Motwani,et al. The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.
[68] Edward R. Dougherty,et al. Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network , 2010, BMC Bioinformatics.
[69] Ralf Bender,et al. Generating survival times to simulate Cox proportional hazards models by Ralf Bender, Thomas Augustin and Maria Blettner, Statistics in Medicine 2005; 24:1713–1723 , 2006, Statistics in medicine.
[70] Tobias Müller,et al. Identifying functional modules in protein–protein interaction networks: an integrated exact approach , 2008, ISMB.