Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines
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N. Cox | R. S. Huang | Paul Geeleher | R. Huang | N. Cox | P. Geeleher
[1] I. Jolliffe. A Note on the Use of Principal Components in Regression , 1982 .
[2] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[3] S. Jagannath,et al. CRITERIA FOR EVALUATING DISEASE RESPONSE AND PROGRESSION IN PATIENTS WITH MULTIPLE MYELOMA TREATED BY HIGH‐DOSE THERAPY AND HAEMOPOIETIC STEM CELL TRANSPLANTATION , 1998, British journal of haematology.
[4] R. Wooster. The cancer genome project , 2002 .
[5] Friedrich Leisch,et al. Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis , 2002, COMPSTAT.
[6] R. Tibshirani,et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[7] Van,et al. A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.
[8] Rafael A Irizarry,et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.
[9] Syed Mohsin,et al. Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer , 2003, The Lancet.
[10] Adrian Wiestner,et al. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[11] Patricia L. Harris,et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. , 2004, The New England journal of medicine.
[12] S. Gabriel,et al. EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy , 2004, Science.
[13] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[14] Benjamin M. Bolstad,et al. affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..
[15] R. Tibshirani,et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[16] Thomas Lengauer,et al. ROCR: visualizing classifier performance in R , 2005, Bioinform..
[17] M. Ostland,et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[18] R. Myers,et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data , 2005, Nucleic acids research.
[19] S. Bicciato,et al. Molecular classification of multiple myeloma: a distinct transcriptional profile characterizes patients expressing CCND1 and negative for 14q32 translocations. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[20] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[21] Daniel Birnbaum,et al. Gene expression profiling identifies molecular subtypes of inflammatory breast cancer. , 2005, Cancer research.
[22] John Crowley,et al. The molecular classification of multiple myeloma. , 2006, Blood.
[23] M. J. van de Vijver,et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.
[24] Maqc Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements , 2006, Nature Biotechnology.
[25] Douglas G Altman,et al. Dichotomizing continuous predictors in multiple regression: a bad idea , 2006, Statistics in medicine.
[26] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[27] Arnoldo Frigessi,et al. BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm305 Gene expression Predicting survival from microarray data—a comparative study , 2022 .
[28] Anthony Boral,et al. Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. , 2006, Blood.
[29] Sean R. Davis,et al. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..
[30] C. Sawyers. The cancer biomarker problem , 2008, Nature.
[31] Sang Hong Lee,et al. Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data , 2008, PLoS genetics.
[32] L. Schwartz,et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.
[33] Anne-Laure Boulesteix,et al. Survival prediction using gene expression data: A review and comparison , 2009, Comput. Stat. Data Anal..
[34] Z. Szallasi,et al. Efficacy of neoadjuvant Cisplatin in triple-negative breast cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[35] Yuqiu Jiang,et al. Personalized medicine in oncology: tailoring the right drug to the right patient. , 2010, Biomarkers in medicine.
[36] Mukesh Verma,et al. Cancer Biomarkers: Are We Ready for the Prime Time? , 2010, Cancers.
[37] Chia Huey Ooi,et al. Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy. , 2011, Gastroenterology.
[38] Faramarz Valafar,et al. Empirical comparison of cross-platform normalization methods for gene expression data , 2011, BMC Bioinformatics.
[39] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[40] Edward S. Kim,et al. The BATTLE trial: personalizing therapy for lung cancer. , 2011, Cancer discovery.
[41] Rafael Sirera,et al. The Role of Tumor Stroma in Cancer Progression and Prognosis: Emphasis on Carcinoma-Associated Fibroblasts and Non-small Cell Lung Cancer , 2011, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.
[42] S. Ramaswamy,et al. Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.
[43] Adam A. Margolin,et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.
[44] Michael Peyton,et al. An Epithelial–Mesenchymal Transition Gene Signature Predicts Resistance to EGFR and PI3K Inhibitors and Identifies Axl as a Therapeutic Target for Overcoming EGFR Inhibitor Resistance , 2012, Clinical Cancer Research.
[45] Daniel Gianola,et al. Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster , 2012, PLoS genetics.
[46] Maqc Consortium. The MicroArray Quality Control ( MAQC )-II study of common practices for the development and validation of microarray-based predictive models , 2012 .
[47] Mira Ayadi,et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value , 2013, PLoS medicine.
[48] Julio Saez-Rodriguez,et al. Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties , 2012, PloS one.
[49] Xiao Xu,et al. Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets , 2013, BMC Bioinformatics.
[50] Jay Shendure,et al. The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line , 2013, Nature.
[51] Erika Cule,et al. Ridge Regression in Prediction Problems: Automatic Choice of the Ridge Parameter , 2013, Genetic epidemiology.
[52] Richard Simon,et al. Implementing personalized cancer genomics in clinical trials , 2013, Nature Reviews Drug Discovery.
[53] Cynthia H Zhang,et al. Maximizing the commercial value of personalized therapeutics and companion diagnostics , 2013, Nature Biotechnology.
[54] Jeroen F. J. Laros,et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories , 2013, Nature Biotechnology.
[55] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[56] Hae Kyung Im,et al. Poly‐Omic Prediction of Complex Traits: OmicKriging , 2013, Genetic epidemiology.
[57] R. S. Huang,et al. Abstract 5561: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines , 2014 .