Weigelt B, Baehner FL, Reis JS: The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. Journal of Pathology. 2010, 220: 263-280.
Article
CAS
PubMed
Google Scholar
Harbeck N, Sotlar K, Wuerstlein R, Doisneau-Sixou S: Molecular and protein markers for clinical decision making in breast cancer: Today and tomorrow. Cancer treatment reviews. 2014, 40: 434-444. 10.1016/j.ctrv.2013.09.014.
Article
CAS
PubMed
Google Scholar
Timar J, Gyorffy B, Raso E: Gene signature of the metastatic potential of cutaneous melanoma: too much for too little?. Clinical and Experimental Metastasis. 2010, 27: 371-387. 10.1007/s10585-010-9307-2.
Article
CAS
PubMed
Google Scholar
Sanz-Pamplona R, Berenguer A, Cordero D, Riccadonna S, Solé X, Crous-Bou M, Guinó E, Sanjuan X, Biondo S, Soriano A, et al: Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review. PLoS ONE. 2012, 7: e48877-10.1371/journal.pone.0048877.
Article
PubMed Central
CAS
PubMed
Google Scholar
Subramanian J, Simon R: Gene Expression-Based Prognostic Signatures in Lung Cancer: Ready for Clinical Use?. J Natl Cancer Inst. 2010, 102: 464-474. 10.1093/jnci/djq025.
Article
PubMed Central
CAS
PubMed
Google Scholar
Emmert-Streib F, Tripathi S, Matos Simoes Rd: Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods. Biology Direct. 2012, 7: 44-10.1186/1745-6150-7-44.
Article
PubMed Central
CAS
PubMed
Google Scholar
Ideker T, Krogan NJ: Differential network biology. Molecular Systems Biology. 2012, 8: 1-9.
Article
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. Nature Biotechnology. 2009, 27: 199-204. 10.1038/nbt.1522.
Article
CAS
PubMed
Google Scholar
Han J-DJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJM, Cusick ME, Roth FP, Vidal M: Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature. 2004, 430: 88-93. 10.1038/nature02555.
Article
CAS
PubMed
Google Scholar
Schramm S-J, Li SS, Jayaswal V, Fung DCY, Campain AE, Pang CNI, Scolyer RA, Yang YH, Mann GJ, Wilkins MR: Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden. Pigment Cell Melanoma Res. 2013, 26: 708-722. 10.1111/pcmr.12126.
Article
CAS
PubMed
Google Scholar
Winter C, Kristiansen G, Kersting S, Roy J, Aust D, Knösel T, Rümmele P, Jahnke B, Hentrich V, Rückert F, et al: Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology. 2012, 8: e1002511-10.1371/journal.pcbi.1002511.
Article
PubMed Central
CAS
PubMed
Google Scholar
Chen J, Sam L, Huang Y, Lee Y, Li J, Liu Y, Xing HR, Lussier YA: Protein interaction network underpins concordant prognosis among heterogeneous breast cancer signatures. Journal of Biomedical Informatics. 2010, 43: 385-396. 10.1016/j.jbi.2010.03.009.
Article
PubMed Central
CAS
PubMed
Google Scholar
Mann GJ, Pupo GM, Campain AE, Carter CD, Schramm S-J, Pianova S, Gerega SK, De Silva C, Lai K, Wilmott JS, et al: BRAF Mutation, NRAS Mutation, and the Absence of an Immune-Related Expressed Gene Profile Predict Poor Outcome in Patients with Stage III Melanoma. Journal of Investigative Dermatology. 2013, 133: 509-517. 10.1038/jid.2012.283.
Article
CAS
PubMed
Google Scholar
Jayawardana K, Schramm S-J, Haydu L, Thompson JF, Scolyer RA, Mann GJ, Müller S, Yang JYH: Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. Int J Cancer.
Bonome T, Levine DA, Shih J, Randonovich M, Pise-Masison CA, Bogomolniy F, Ozbun L, Brady J, Barrett JC, Boyd J, Birrer MJ: A Gene Signature Predicting for Survival in Suboptimally Debulked Patients with Ovarian Cancer. Cancer Research. 2008, 68: 5478-5486. 10.1158/0008-5472.CAN-07-6595.
Article
CAS
PubMed
Google Scholar
Breiman L: Random Forests. Machine Learning. 2001, 45: 5-32. 10.1023/A:1010933404324.
Article
Google Scholar
Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2009, Springer, 2
Chapter
Google Scholar
Burges CC: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 1998, 2: 121-167. 10.1023/A:1009715923555.
Article
Google Scholar
Staiger C, Cadot S, Györffy B, Wessels LFA, Klau GW: Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis. Frontiers in Genetics. 2013, 4:
Google Scholar
Staiger C, Cadot S, Kooter R, Dittrich M, Müller T, Klau GW, Wessels LFA: A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer. PLoS ONE. 2012, 7: e34796-10.1371/journal.pone.0034796.
Article
PubMed Central
CAS
PubMed
Google Scholar
Cun Y, Frohlich H: Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions. BMC Bioinformatics. 2012, 13: 69-10.1186/1471-2105-13-69.
Article
PubMed Central
CAS
PubMed
Google Scholar
Schena M, Shalon D, Heller R, Chai A, Brown PO, Davis RW: Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proceedings of the National Academy of Sciences. 1996, 93: 10614-10619. 10.1073/pnas.93.20.10614.
Article
CAS
Google Scholar
DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997, 278: 680-686. 10.1126/science.278.5338.680.
Article
CAS
PubMed
Google Scholar
Dosztányi Z, Chen J, Dunker AK, Simon I, Tompa P: Disorder and Sequence Repeats in Hub Proteins and Their Implications for Network Evolution. Journal of Proteome Research. 2006, 5: 2985-2995. 10.1021/pr060171o.
Article
PubMed
Google Scholar
Callow MJ, Dudoit S, Gong EL, Speed TP, Rubin EM: Microarray Expression Profiling Identifies Genes with Altered Expression in HDL-Deficient Mice. Genome Research. 2000, 10: 2022-2029. 10.1101/gr.10.12.2022.
Article
PubMed Central
CAS
PubMed
Google Scholar
Cui X, Churchill G: Statistical tests for differential expression in cDNA microarray experiments. Genome Biology. 2003, 4: 210-10.1186/gb-2003-4-4-210.
Article
PubMed Central
PubMed
Google Scholar
Kerr KM, Martin M, Churchill GA: Analysis of variance for gene expression microarray data. Journal of Computational Biology. 2000, 7: 819-837. 10.1089/10665270050514954.
Article
CAS
PubMed
Google Scholar
Wright GW, Simon RM: A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics. 2003, 19: 2448-2455. 10.1093/bioinformatics/btg345.
Article
CAS
PubMed
Google Scholar
Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences. 2001, 98: 5116-5121. 10.1073/pnas.091062498.
Article
CAS
Google Scholar
Smyth GK: limma: Linear Models for Microarray Data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Edited by: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S. 2005, Springer New York, 397-420. Statistics for Biology and Health
Chapter
Google Scholar
Jeanmougin M, de Reynies A, Marisa L, Paccard C, Nuel G, Guedj M: Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies. PLoS ONE. 2010, 5: e12336-10.1371/journal.pone.0012336.
Article
PubMed Central
PubMed
Google Scholar
Subramanian A, Tamayo AP, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.
Article
PubMed Central
CAS
PubMed
Google Scholar
Dørum G, Snipen L, Solheim M, Sæbø S: Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments. Statistical Applications in Genetics and Molecular Biology. 2009, 8: 1-
Article
Google Scholar
Efron B, Tibshirani R: On Testing the Significance of Sets of Genes. Annals of Applied Statistics. 2007, 1: 107-129. 10.1214/07-AOAS101.
Article
Google Scholar
Newton MA, Quintana FA, den Boon JA, Sengupta S, Ahlquist P: Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Annals of Applied Statistics. 2007, 1: 85-106. 10.1214/07-AOAS104.
Article
Google Scholar
Luo W, Friedman M, Shedden K, Hankenson K, Woolf P: GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics. 2009, 10: 161-10.1186/1471-2105-10-161.
Article
PubMed Central
PubMed
Google Scholar
Chen SX, Zhang LX, Zhong PS: Tests for High-Dimensional Covariance Matrices. Journal of the American Statistical Association. 2010, 105: 810-819. 10.1198/jasa.2010.tm09560.
Article
CAS
Google Scholar
Drier Y, Sheffer M, Domany E: Pathway-based personalized analysis of cancer. Proceedings of the National Academy of Sciences. 2013, 110: 6388-6393. 10.1073/pnas.1219651110.
Article
CAS
Google Scholar
Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Molecular Systems Biology. 2007, 3:
Google Scholar
Zhe S, Naqvi SAZ, Yang Y, Qi Y: Joint network and node selection for pathway-based genomic data analysis. Bioinformatics. 2013
Google Scholar
Jay J, Eblen J, Zhang Y, Benson M, Perkins A, Saxton A, Voy B, Chesler E, Langston M: A systematic comparison of genome-scale clustering algorithms. BMC Bioinformatics. 2012, 13: S7-
Article
PubMed Central
PubMed
Google Scholar
Zhang B, Horvath S: A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology. 2005, 4: Article 17-
Article
Google Scholar
Morrison J, Breitling R, Higham D, Gilbert D: GeneRank: Using search engine technology for the analysis of microarray experiments. BMC Bioinformatics. 2005, 6: 233-10.1186/1471-2105-6-233.
Article
PubMed Central
PubMed
Google Scholar
Shi M, Beauchamp RD, Zhang B: A Network-Based Gene Expression Signature Informs Prognosis and Treatment for Colorectal Cancer Patients. PLoS ONE. 2012, 7: e41292-10.1371/journal.pone.0041292.
Article
PubMed Central
CAS
PubMed
Google Scholar
Wu X, Jiang R, Zhang MQ, Li S: Network-based global inference of human disease genes. Molecular Systems Biology. 2008, 4: 189-
Article
PubMed Central
PubMed
Google Scholar
Ma S, Shi M, Li Y, Yi D, Shia B-C: Incorporating gene co-expression network in identification of cancer prognosis markers. BMC Bioinformatics. 2010, 11: 271-10.1186/1471-2105-11-271.
Article
PubMed Central
PubMed
Google Scholar
Rapaport F, Zinovyev A, Dutreix M, Barillot E, Vert J-P: Classification of microarray data using gene networks. BMC Bioinformatics. 2007, 8: 35-10.1186/1471-2105-8-35.
Article
PubMed Central
PubMed
Google Scholar
Teschendorff A, Severini S: Increased entropy of signal transduction in the cancer metastasis phenotype. BMC Systems Biology. 2010, 4: 104-10.1186/1752-0509-4-104.
Article
PubMed Central
PubMed
Google Scholar
West J, Bianconi G, Severini S, Teschendorff AE: Differential network entropy reveals cancer system hallmarks. Scientific Reports. 2012, 2:
Google Scholar
Turner B, Razick S, Turinsky AL, Vlasblom J, Crowdy EK, Cho E, Morrison K, Donaldson IM, Wodak SJ: iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database. 2010, 2010: 1-15.
Article
Google Scholar
Fan RE, Chen PH, Lin CJ: Working Set Selection Using Second Order Information for Training Support Vector Machines. The Journal of Machine Learning Research. 2005, 6: 1889-1918.
Google Scholar
Dettling M, Buhlmann P: Supervised clustering of genes. Genome Biology. 2002, 3: research0069.0061-research0069.0015.
Article
Google Scholar
R_Core_Team: R: A Language and Environment for Statistical Computing. 2014, Vienna, Austria: R Foundation for Statistical Computing
Google Scholar
Martinez JG, Carroll RJ, Müller S, Sampson JN, Chatterjee N: Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context. The American Statistician. 2011, 65: 223-228. 10.1198/tas.2011.11052.
Article
PubMed Central
PubMed
Google Scholar
Roy J, Winter C, Isik Z, Schroeder M: Network information improves cancer outcome prediction. Briefings in Bioinformatics. 2012
Google Scholar
Zeng T, Sun S-y, Wang Y, Zhu H, Chen L: Network biomarkers reveal dysfunctional gene regulations during disease progression. FEBS Journal. 2013, 280: 5682-5695. 10.1111/febs.12536.
Article
CAS
PubMed
Google Scholar
Sanavia T, Aiolli F, Da San Martino G, Bisognin A, Di Camillo B: Improving biomarker list stability by integration of biological knowledge in the learning process. BMC Bioinformatics. 2012, 13:
Google Scholar
Mathivanan S, Periaswamy B, Gandhi T, Kandasamy K, Suresh S, Mohmood R, Ramachandra Y, Pandey A: An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics. 2006, 7: S19-
Article
PubMed Central
PubMed
Google Scholar
De Las Rivas J, Fontanillo C: Protein-Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks. PLoS Computational Biology. 2010, 6: e1000807-10.1371/journal.pcbi.1000807.
Article
PubMed Central
PubMed
Google Scholar
Kirouac D, Saez-Rodriguez J, Swantek J, Burke J, Lauffenburger D, Sorger P: Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Systems Biology. 2012, 6: 29-10.1186/1752-0509-6-29.
Article
PubMed Central
PubMed
Google Scholar
Schramm S-J, Jayaswal V, Goel A, Li SS, Yang YH, Mann GJ, Wilkins MR: Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations. Proteomics. 2013, 13: 3393-3405. 10.1002/pmic.201200570.
Article
CAS
PubMed
Google Scholar
Brückner A, Polge C, Lentze N, Auerbach D, Schlattner U: Yeast Two-Hybrid, a Powerful Tool for Systems Biology. International Journal of Molecular Sciences. 2009, 10: 2763-2788. 10.3390/ijms10062763.
Article
PubMed Central
PubMed
Google Scholar
Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-1178. 10.1038/nature04209.
Article
CAS
PubMed
Google Scholar