Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies
暂无分享,去创建一个
Björn Nilsson | Thoas Fioretos | Sven Nelander | B. Nilsson | T. Fioretos | S. Nelander | Björn Nilsson | Mikael Johansson | Petra Håkansson | Mikael Johansson | P. Håkansson | Sven Nelander | B. Nilsson | Petra Håkansson
[1] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[2] AN Kolmogorov-Smirnov,et al. Sulla determinazione empírica di uma legge di distribuzione , 1933 .
[3] Hongyue Dai,et al. Gene expression changes associated with progression and response in chronic myeloid leukemia. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[4] X. Cui,et al. Improved statistical tests for differential gene expression by shrinking variance components estimates. , 2005, Biostatistics.
[5] D. Darling,et al. A Test of Goodness of Fit , 1954 .
[6] Andrew B. Nobel,et al. Significance analysis of functional categories in gene expression studies: a structured permutation approach , 2005, Bioinform..
[7] J. Downing,et al. Classification of pediatric acute lymphoblastic leukemia by gene expression profiling. , 2003, Blood.
[8] Alex E. Lash,et al. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..
[9] T. Golub,et al. A Mechanism of Cyclin D1 Action Encoded in the Patterns of Gene Expression in Human Cancer , 2003, Cell.
[10] M. Daly,et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.
[11] M Giehl,et al. Gene expression profiling of CD34+ cells identifies a molecular signature of chronic myeloid leukemia blast crisis , 2006, Leukemia.
[12] Patrik Edén,et al. Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[13] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[14] Paul Pavlidis,et al. ErmineJ: Tool for functional analysis of gene expression data sets , 2005, BMC Bioinformatics.
[15] E. Lander,et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[16] R. Verhaak,et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. , 2004, The New England journal of medicine.
[17] B. Druker,et al. Oncogenes and Tumor Suppressors (795 articles) , 2004 .
[18] Patrik Edén,et al. Comparing Functional Annotation Analyses with Catmap Comparing Functional Annotation Analyses with Catmap , 2004 .
[19] R. Spang,et al. Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[20] Hagai Bergman,et al. Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression , 2005, Bioinform..
[21] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[22] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[23] A. Martin-Löf. On the composition of elementary errors , 1994 .
[24] Gordon K Smyth,et al. Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.
[25] Jin Zhang. Powerful goodness‐of‐fit tests based on the likelihood ratio , 2002 .
[26] Purvesh Khatri,et al. Ontological analysis of gene expression data: current tools, limitations, and open problems , 2005, Bioinform..