TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data
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David J. Harrison | Alexander L. R. Lubbock | Ian M. Overton | Elad Katz | I. Overton | D. Harrison | E. Katz
[1] David R. Westhead,et al. TmaDB: a repository for tissue microarray data , 2005, BMC Bioinformatics.
[2] N. Jamieson,et al. Tissue Biomarkers for Prognosis in Pancreatic Ductal Adenocarcinoma: A Systematic Review and Meta-analysis , 2011, Clinical Cancer Research.
[3] Xueli Liu,et al. Statistical Methods for Analyzing Tissue Microarray Data , 2004, Journal of biopharmaceutical statistics.
[4] D. E. Roberts,et al. The Upper Tail Probabilities of Spearman's Rho , 1975 .
[5] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[6] J. Yates,et al. Large-scale analysis of the yeast proteome by multidimensional protein identification technology , 2001, Nature Biotechnology.
[7] Gary D. Bader,et al. Cytoscape Web: an interactive web-based network browser , 2010, Bioinform..
[8] Koen J. F. Verhoeven,et al. Implementing false discovery rate control: increasing your power , 2005 .
[9] Jesper Tegnér,et al. Reverse engineering gene networks using singular value decomposition and robust regression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[10] D. Schoenfeld. The asymptotic properties of nonparametric tests for comparing survival distributions , 1981 .
[11] A. Barabasi,et al. Interactome Networks and Human Disease , 2011, Cell.
[12] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[13] K. Kinzler,et al. Cancer genes and the pathways they control , 2004, Nature Medicine.
[14] D. Allred,et al. Prognostic and predictive factors in breast cancer by immunohistochemical analysis. , 1998, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc.
[15] Paul Pavlidis,et al. The role of indirect connections in gene networks in predicting function , 2011, Bioinform..
[16] A. Barabasi,et al. Network medicine : a network-based approach to human disease , 2010 .
[17] Bernd Lahrmann,et al. Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers , 2011, PloS one.
[18] Ash A. Alizadeh,et al. Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays. , 2002, The American journal of pathology.
[19] M. Salto‐Tellez,et al. Reliability of Tissue Microarrays in Detecting Protein Expression and Gene Amplification in Breast Cancer , 2003, Modern Pathology.
[20] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[21] D. Rimm,et al. X-Tile , 2004, Clinical Cancer Research.
[22] E B Cox,et al. Use of a monoclonal anti-estrogen receptor antibody in the immunohistochemical evaluation of human tumors. , 1986, Cancer research.
[23] Walter Kolch,et al. Functional proteomics to dissect tyrosine kinase signalling pathways in cancer , 2010, Nature Reviews Cancer.
[24] S Detre,et al. A "quickscore" method for immunohistochemical semiquantitation: validation for oestrogen receptor in breast carcinomas. , 1995, Journal of clinical pathology.
[25] P. Sham,et al. Multiple testing and power calculations in genetic association studies. , 2011, Cold Spring Harbor protocols.
[26] E. Petricoin,et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front , 2001, Oncogene.
[27] David Venet,et al. Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome , 2011, PLoS Comput. Biol..
[28] J. Rao,et al. Protein expression analysis using quantitative fluorescence image analysis on tissue microarray slides. , 2002, BioTechniques.
[29] Jim Davies,et al. A metadata-aware application for remote scoring and exchange of tissue microarray images , 2013, BMC Bioinformatics.
[30] Igor Goryanin,et al. Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab. , 2009, Cancer research.
[31] E. Kaplan,et al. Nonparametric Estimation from Incomplete Observations , 1958 .
[32] E. Lander,et al. Loss of E-cadherin promotes metastasis via multiple downstream transcriptional pathways. , 2008, Cancer research.
[33] Ian Abramson. On Bandwidth Variation in Kernel Estimates-A Square Root Law , 1982 .
[34] Johan Lindberg,et al. Correlation Network Analysis for Data Integration and Biomarker Selectionw , 2007 .
[35] N. Mantel. Evaluation of survival data and two new rank order statistics arising in its consideration. , 1966, Cancer chemotherapy reports.
[36] M. Takeichi,et al. Expression of E-cadherin cell adhesion molecules in human breast cancer tissues and its relationship to metastasis. , 1993, Cancer research.
[37] T G Clark,et al. Survival Analysis Part I: Basic concepts and first analyses , 2003, British Journal of Cancer.
[38] Kakajan Komurov,et al. Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes , 2010, Proceedings of the National Academy of Sciences.
[39] J. Kononen,et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens , 1998, Nature Medicine.
[40] Martina Uray,et al. TAMEE: data management and analysis for tissue microarrays , 2007, BMC Bioinformatics.
[41] Julio R. Banga,et al. Inference of complex biological networks: distinguishability issues and optimization-based solutions , 2011, BMC Systems Biology.
[42] David L Rimm,et al. Quantitative analysis of breast cancer tissue microarrays shows that both high and normal levels of HER2 expression are associated with poor outcome. , 2003, Cancer research.
[43] R. Huang,et al. Epithelial-Mesenchymal Transitions in Development and Disease , 2009, Cell.
[44] J. Cuzick,et al. Prognostic Value of a Combined ER, PgR, Ki67, HER2 Immunohistochemical (IHC4) Score and Comparison with the GHI Recurrence Score – Results from TransATAC. , 2009 .
[45] Joshua M. Stuart,et al. A Gene Expression Map for Caenorhabditis elegans , 2001, Science.
[46] D. Rimm,et al. A decade of tissue microarrays: progress in the discovery and validation of cancer biomarkers. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[47] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[48] D. Harrison,et al. Two possible mechanisms of epithelial to mesenchymal transition in invasive ductal breast cancer , 2011, Clinical & Experimental Metastasis.
[49] M. Rosenblatt. Remarks on Some Nonparametric Estimates of a Density Function , 1956 .
[50] Alberto Riva,et al. Internet-based profiler system as integrative framework to support translational research , 2005, BMC Bioinformatics.
[51] David M. Simcha,et al. Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.
[52] D. Rimm,et al. Automated quantitative analysis (AQUA) of in situ protein expression, antibody concentration, and prognosis. , 2005, Journal of the National Cancer Institute.
[53] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[54] R. Meehan,et al. An In Vitro Model That Recapitulates the Epithelial to Mesenchymal Transition (EMT) in Human Breast Cancer , 2011, PloS one.
[55] D. Rimm,et al. Validation of Tissue Microarray Technology in Breast Carcinoma , 2000, Laboratory Investigation.