Gaining confidence in biological interpretation of the microarray data: the functional consistence of the significant GO categories
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Jing Zhu | Hui Xiao | Min Zhang | Qing Liu | Chen Yao | Baofeng Yang | Da Yang | Zheng Guo | Yanhui Li | Jing Wang | Dong Wang | Wencai Ma | M. Zhang | Baofeng Yang | Yanhui Li | Dong Wang | Jing Wang | D. Yang | Wencai Ma | Zheng Guo | Jing Zhu | Chen Yao | Qing Liu | H. Xiao
[1] Björn Nilsson,et al. Threshold-free high-power methods for the ontological analysis of genome-wide gene-expression studies , 2007, Genome Biology.
[2] Olivier Bodenreider,et al. Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.
[3] R. Tibshirani,et al. Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[4] Alex Lewin,et al. Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data , 2006, BMC Bioinformatics.
[5] Jing Zhu,et al. Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network , 2007, Bioinform..
[6] Thomas Lengauer,et al. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure , 2006, Bioinform..
[7] Stanley N Cohen,et al. Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[8] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[9] Philip Resnik,et al. Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..
[10] Petri Törönen,et al. Theme discovery from gene lists for identification and viewing of multiple functional groups , 2005, BMC Bioinformatics.
[11] Jelle J. Goeman,et al. A global test for groups of genes: testing association with a clinical outcome , 2004, Bioinform..
[12] Douglas A. Hosack,et al. Identifying biological themes within lists of genes with EASE , 2003, Genome Biology.
[13] Joaquín Dopazo,et al. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes , 2004, Bioinform..
[14] Holger Fröhlich,et al. GOSim – an R-package for computation of information theoretic GO similarities between terms and gene products , 2007, BMC Bioinformatics.
[15] Charles F. Hockett,et al. A mathematical theory of communication , 1948, MOCO.
[16] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[17] Benno Schwikowski,et al. Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.
[18] R. Verhaak,et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. , 2004, The New England journal of medicine.
[19] Carole A. Goble,et al. Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation , 2003, Bioinform..
[20] Jing Zhu,et al. Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network , 2007, Bioinform..
[21] Terence P. Speed,et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..
[22] Rafael A Irizarry,et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.
[23] Dekang Lin,et al. An Information-Theoretic Definition of Similarity , 1998, ICML.
[24] P. Khatri,et al. Global functional profiling of gene expression ? ? This work was funded in part by a Sun Microsystem , 2003 .
[25] Jing Zhu,et al. Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules , 2006, Bioinform..
[26] L. R. Rasmussen,et al. In information retrieval: data structures and algorithms , 1992 .
[27] S. Dudoit,et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. , 2002, Nucleic acids research.
[28] P. Khatri,et al. Global functional profiling of gene expression. , 2003, Genomics.
[29] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[30] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .
[31] D. Botstein,et al. Gene expression patterns in human liver cancers. , 2002, Molecular biology of the cell.
[32] Dong Wang,et al. GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology , 2007, BMC Genomics.
[33] Russ B. Altman,et al. Missing value estimation methods for DNA microarrays , 2001, Bioinform..
[34] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[35] Hagai Bergman,et al. Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression , 2005, Bioinform..
[36] 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.