Nearest Neighbor Networks: clustering expression data based on gene neighborhoods
暂无分享,去创建一个
Matthew A. Hibbs | Kellen L. Olszewski | H. Coller | C. Huttenhower | O. Troyanskaya | C. Myers | N. Siemers | A. Flamholz | Jess N. Landis | S. Sahi
[1] Olga G. Troyanskaya,et al. A scalable method for integration and functional analysis of multiple microarray datasets , 2006, Bioinform..
[2] Matthew A. Hibbs,et al. Finding function: evaluation methods for functional genomic data , 2006, BMC Genomics.
[3] Michael A. Langston,et al. Extracting Gene Networks for Low-Dose Radiation Using Graph Theoretical Algorithms , 2006, PLoS Comput. Biol..
[4] Lei Liu,et al. Comparisons of graph-structure clustering methods for gene expression data. , 2006, Acta biochimica et biophysica Sinica.
[5] D. Allison,et al. Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.
[6] Vito Di Gesù,et al. GenClust: A genetic algorithm for clustering gene expression data , 2005, BMC Bioinformatics.
[7] Martin Ester,et al. Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses. , 2005, Genomics.
[8] Joseph Beyene,et al. Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models , 2005, BMC Bioinformatics.
[9] Rachel B. Brem,et al. The landscape of genetic complexity across 5,700 gene expression traits in yeast. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[10] T. Ideker,et al. Integrating phenotypic and expression profiles to map arsenic-response networks , 2004, Genome Biology.
[11] Alok J. Saldanha,et al. Java Treeview - extensible visualization of microarray data , 2004, Bioinform..
[12] Roded Sharan,et al. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[13] Gary D Bader,et al. Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.
[14] Joshua M. Stuart,et al. A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.
[15] B. De Moor,et al. Comparison and meta-analysis of microarray data: from the bench to the computer desk. , 2003, Trends in genetics : TIG.
[16] Ron Shamir,et al. CLICK and EXPANDER: a system for clustering and visualizing gene expression data , 2003, Bioinform..
[17] Sangsoo Kim,et al. Combining multiple microarray studies and modeling interstudy variation , 2003, ISMB.
[18] X. Cui,et al. Statistical tests for differential expression in cDNA microarray experiments , 2003, Genome Biology.
[19] A I Saeed,et al. TM4: a free, open-source system for microarray data management and analysis. , 2003, BioTechniques.
[20] Partha S. Vasisht. Computational Analysis of Microarray Data , 2003 .
[21] M. Eisen,et al. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering , 2002, Genome Biology.
[22] Roded Sharan,et al. Discovering statistically significant biclusters in gene expression data , 2002, ISMB.
[23] Roberto Marcondes Cesar Junior,et al. Inference from Clustering with Application to Gene-Expression Microarrays , 2002, J. Comput. Biol..
[24] Russ B. Altman,et al. Missing value estimation methods for DNA microarrays , 2001, Bioinform..
[25] Kevin R. Coombes,et al. Identifying Differentially Expressed Genes in cDNA Microarray Experiments , 2001, J. Comput. Biol..
[26] Ronald W. Davis,et al. The core meiotic transcriptome in budding yeasts , 2000, Nature Genetics.
[27] D. Botstein,et al. Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.
[28] George M. Church,et al. Biclustering of Expression Data , 2000, ISMB.
[29] Hongyue Dai,et al. Widespread aneuploidy revealed by DNA microarray expression profiling , 2000, Nature Genetics.
[30] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[31] Trey Ideker,et al. Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data , 2000, J. Comput. Biol..
[32] Laurie J. Heyer,et al. Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.
[33] G. Church,et al. Systematic determination of genetic network architecture , 1999, Nature Genetics.
[34] Zohar Yakhini,et al. Clustering gene expression patterns , 1999, J. Comput. Biol..
[35] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[36] Michael Ruogu Zhang,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.
[37] M. N. S. Swamy,et al. Graphs: Theory and Algorithms: Thulasiraman/Graphs , 1992 .
[38] M. N. Shanmukha Swamy,et al. Graphs - theory and algorithms , 1992 .
[39] Eitan M. Gurari,et al. Introduction to the theory of computation , 1989 .
[40] Kenneth H. Rosen. Discrete mathematics and its applications , 1984 .
[41] H. Toutenburg,et al. Lehmann, E. L., Nonparametrics: Statistical Methods Based on Ranks, San Francisco. Holden‐Day, Inc., 1975. 480 S., $ 22.95 . , 1977 .
[42] Robert E. Tarjan,et al. Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..
[43] Bernard Harris,et al. Graph theory and its applications , 1970 .
[44] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[45] Robert R. Sokal,et al. A statistical method for evaluating systematic relationships , 1958 .