An integrated approach for identifying genes associated with chemotherapy resistance in high-grade serous epithelial ovarian cancer
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Koti M, Gooding RJ, Nuin P, Haslehurst A, Crane C, et al. Identification of the IGF1/PI3K/NFB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancer. BMC Cancer 2013; 13: 549. doi: 10.1186/1471-2407-13-549.
Cannistra SA. Cancer of the ovary. N Engl J Med 2004; 351: 2519–2529. doi: 10.1056/NEJMra041842.
Dressman HK, Berchuck A, Chan G, Zhai J, Bild A, et al. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. J Clin Oncol 2007; 25: 517–525. doi: 10.1200/JCO.2006.06.3743.
Hossain A, Khan HTA. Identification of genomic markers correlated with sensitivity in solid tumors to Dasatinib using sparse principal components. J Applied Statistics 2016, 43(14): 2538–2549. doi: 10.1080/02664763.2016.1142941.
Tusher VG, Tibshirani R, Chu G. Significance analysis of microarray applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001; 98(9): 5116–5121. doi: 10.1073/pnas.091062498.
Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiment. Stat Appl Genet Mol Biol 2004; 3(1): 1–25. doi: 10.2202/1544-6115.1027.
Smyth GK. Limma: Linear models for microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S (editors). Bioinformatics and computational biology solutions using R and bioconductor. New York: Springer; 2005. p. 397–420. doi: 10.1007/0-387-29362-0_23.
Efron B, Tibshirani R, Storey JD, Tusher V. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001; 96(456): 1151–1160.
Pepe MS, Longton G, Anderson GL, Schummer M. Selecting differentially expressed genes from microarray experiments. Biometrics 2003; 59: 133–142. doi: 10.1111/1541-0420.00016.
Hossain A, Beyene J. An improved method on Wilcoxon rank sum test for gene selection from microarray experiments. Commun Stat Simul Comput2013; 42 (7): 1563–1577. doi: 10.1080/03610918.2012.667479.
Hossain A, Beyene J. Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data. Stat Appl Genet Mol Biol 2013; 12(6): 743–755. doi: 10.1515/sagmb-2013-0035.
Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat 2006; 15: 265–286. doi: 10.1198/106186006X113430.
Witten D, Tibshirani R, Hastie T. A penalized matrix decomposition, with application to sparse principal components and canonical correlation analysis. Biostatistics 2009; 10: 515–534. doi: 10.1093/biostatistics/kxp008.
Tibshirani R, Chu G, Narasimhan B, Li J. samr: SAM: Significance analysis of microarrays [Internet]. R package version 2.0: Stanford University; 2011 [cited 2016 Oct 24]. Available from: https://CRAN.R-project.org/package=samr.
Troyanskaya OG, Garber M, Brown P, Botstein D, Altman RB. Non-parametric methods for identifying differentially expressed genes in microarray data. Bioinformatics 2002; 18(11): 1454–1461. doi: 10.1093/bioinformatics/18.11.1454.
Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: Application to sporulation time series. Pac Symp Biocomput 2000; 5: 452–463.
Zou H, Hastie T. elasticnet: Elastic-net for sparse estimation and sparse PCA [Internet]. R package version 1.1: University of Minnesota; 2013 [cited 2016 Oct 24]. Available from: http://CRAN.R-project.org/package=elasticnet.
Buja A, Cook D, Swayne DF. Interactive high-dimensional data visualization, J Comput Graph Stat 1996; 5(1): 78–99. doi: 10.1080/10618600.1996.10474696.
Michaels GS, Carr DB, Askenazi M, Fuhrman S, Wen X. et al. Cluster analysis and data visualization of large-scale gene expression data. Pac Symp Biocomput 1998; 3: 42–53.
DOI: http://dx.doi.org/10.30564/amor.v3i1.105
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