Net workconstrainedc lusteringforgene microarraydata
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[1] Alfred O. Hero,et al. High Throughput Screening of Co-Expressed Gene Pairs with Controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS) , 2005, J. Comput. Biol..
[2] Y. Benjamini,et al. False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters , 2005 .
[3] George C Tseng,et al. Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data , 2005, Biometrics.
[4] W. Wong,et al. Functional annotation and network reconstruction through cross-platform integration of microarray data , 2005, Nature Biotechnology.
[5] S. AdhiHarmoko,et al. Introduction to Algorithms , 2005 .
[6] Dongxiao Zhu,et al. BMC Bioinformatics BioMed Central , 2005 .
[7] An-Ping Zeng,et al. Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach , 2004, BMC Bioinformatics.
[8] An-Ping Zeng,et al. Decomposition of metabolic network into functional modules based on the global connectivity structure of reaction graph , 2004, Bioinform..
[9] G. Gibson,et al. Cross-species comparison of genome-wide expression patterns , 2004, Genome Biology.
[10] Homin K. Lee,et al. Coexpression analysis of human genes across many microarray data sets. , 2004, Genome research.
[11] Alfred O. Hero,et al. Multicriteria Gene Screening for Analysis of Differential Expression with DNA Microarrays , 2004, EURASIP J. Adv. Signal Process..
[12] Joshua M. Stuart,et al. A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.
[13] An-Ping Zeng,et al. The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks , 2003, Bioinform..
[14] Julien Gagneur,et al. Hierarchical Analysis of Dependency in Metabolic Networks , 2003, Bioinform..
[15] D. Edwards,et al. Statistical Analysis of Gene Expression Microarray Data , 2003 .
[16] Yoav Benjamini,et al. Identifying differentially expressed genes using false discovery rate controlling procedures , 2003, Bioinform..
[17] Terence P. Speed,et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..
[18] Roger E Bumgarner,et al. Clustering gene-expression data with repeated measurements , 2003, Genome Biology.
[19] W. Wong,et al. Transitive functional annotation by shortest-path analysis of gene expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[20] Adrian E. Raftery,et al. Model-based clustering and data transformations for gene expression data , 2001, Bioinform..
[21] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[22] A. Butte,et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[23] M. Ashburner,et al. Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.
[24] I S Kohane,et al. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
[25] Trey Ideker,et al. Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data , 2000, J. Comput. Biol..
[26] C. Hollenberg,et al. Concurrent knock‐out of at least 20 transporter genes is required to block uptake of hexoses in Saccharomyces cerevisiae , 1999, FEBS letters.
[27] 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.
[28] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[29] A. Jackson,et al. A conserved retina-specific gene encodes a basic motif/leucine zipper domain. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[30] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .
[31] P. Bickel,et al. Mathematical Statistics: Basic Ideas and Selected Topics , 1977 .
[32] William M. Rand,et al. Objective Criteria for the Evaluation of Clustering Methods , 1971 .