expression data analysis Increasing the efficiency of fuzzy logic-based gene

[1]  Mohamad Musavi,et al.  Use of clustering to improve performance in fuzzy gene expression analysis , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[2]  Robert Reynolds,et al.  Improving robustness of fuzzy gene modeling , 2002, ESANN.

[3]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[6]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[7]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[8]  Ron Shamir,et al.  Clustering Gene Expression Patterns , 1999, J. Comput. Biol..

[9]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[10]  Habtom W. Ressom,et al.  Double self-organizing maps to cluster gene expression data , 2002, ESANN.

[11]  Ron Shamir,et al.  An algorithm for clustering cDNAs for gene expression analysis , 1999, RECOMB.

[12]  Reinhard Guthke,et al.  Gene Expression Data Mining for Functional Genomics , 2001 .

[13]  David West,et al.  A comparison of SOM neural network and hierarchical clustering methods , 1996 .

[14]  Michael E. Cusick,et al.  The Yeast Proteome Database (YPD) and Caenorhabditis elegans Proteome Database (WormPD): comprehensive resources for the organization and comparison of model organism protein information , 2000, Nucleic Acids Res..

[15]  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.

[16]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[17]  Daniel Berleant,et al.  Creating Metabolic Network Models using Text Mining and Expert Knowledge , 2003, Computational Biology and Genome Informatics.

[18]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[19]  A. Schuster,et al.  Tumor classification by gene expression profiling: comparison and validation of five clustering methods , 2001, SIGB.

[20]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[21]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

[22]  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.

[23]  Isaac S. Kohane,et al.  On Reporting Fold Differences , 2000, Pacific Symposium on Biocomputing.

[24]  Taizo Hanai,et al.  Gene Expression Analysis Using Fuzzy ART , 2001 .

[25]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[26]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[27]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[28]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.