Systems Level Modeling of Gene Regulatory Networks
暂无分享,去创建一个
Bernd Schürmann | Mathäus Dejori | Martin Stetter | B. Schürmann | M. Stetter | M. Dejori | Mathäus Dejori
[1] Patrik D'haeseleer,et al. Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..
[2] B. Barrell,et al. Life with 6000 Genes , 1996, Science.
[3] S. Hanash,et al. Disease proteomics , 2003, Nature.
[4] Christoph Plass,et al. Cancer epigenomics. , 2002, Human molecular genetics.
[5] Nir Friedman,et al. Data Analysis with Bayesian Networks: A Bootstrap Approach , 1999, UAI.
[6] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[7] S. P. Fodor,et al. Multiplexed biochemical assays with biological chips , 1993, Nature.
[8] E. Davidson,et al. Cis-regulatory logic in the endo16 gene: switching from a specification to a differentiation mode of control. , 2001, Development.
[9] D. Slonim. From patterns to pathways: gene expression data analysis comes of age , 2002, Nature Genetics.
[10] M. Stetter,et al. Hunting drug targets by systems-level modeling of gene expression profiles , 2004, IEEE Transactions on NanoBioscience.
[11] Ronald W. Davis,et al. Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.
[12] Mathäus Dejori,et al. Identifying Interventional and Pathogenic Mechanisms by Generative Inverse Modeling of Gene Expression Profiles , 2004, J. Comput. Biol..
[13] Gustavo Deco,et al. Computational neuroscience of vision , 2002 .
[14] A. Cornish-Bowden. Fundamentals of Enzyme Kinetics , 1979 .
[15] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[16] Reimar Hofmann. Lernen der Struktur nichtlinearer Abhängigkeiten mit graphischen Modellen , 2000 .
[17] M Kato,et al. Inferring genetic networks from DNA microarray data by multiple regression analysis. , 2000, Genome informatics. Workshop on Genome Informatics.
[18] Mathäus Dejori,et al. Bayesian Inference of Genetic Networks from Gene-Expression-Data: Convergence and Reliability , 2003, IC-AI.
[19] R. Somogyi,et al. The gene expression matrix: towards the extraction of genetic network architectures , 1997 .
[20] N. W. Davis,et al. The complete genome sequence of Escherichia coli K-12. , 1997, Science.
[21] R. Brent,et al. Modelling cellular behaviour , 2001, Nature.
[22] Pierre Baldi,et al. Bioinformatics - the machine learning approach (2. ed.) , 2000 .
[23] B L Strehler,et al. Deletional mutations are the basic cause of aging: historical perspectives. , 1995, Mutation research.
[24] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[25] Anders Krogh,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[26] H. Iba,et al. Inferring a system of differential equations for a gene regulatory network by using genetic programming , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).
[27] Michaela Scherr,et al. Gene silencing mediated by small interfering RNAs in mammalian cells. , 2003, Current medicinal chemistry.
[28] Gustavo Deco,et al. Large-Scale Computational Modeling of Genetic Regulatory Networks , 2003, Artificial Intelligence Review.
[29] Werner Dubitzky,et al. A Practical Approach to Microarray Data Analysis , 2003, Springer US.
[30] Michal Linial,et al. Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..
[31] Zlatko Trajanoski,et al. Analyzing Gene-Expression Data with Bayesian Networks , 2002 .
[32] D. Botstein,et al. Exploring the new world of the genome with DNA microarrays , 1999, Nature Genetics.
[33] Jan Vijg,et al. Large genome rearrangements as a primary cause of aging , 2002, Mechanisms of Ageing and Development.
[34] Harald Steck,et al. Constraint-based structural learning in Bayesian networks using finite data sets , 2001 .
[35] Adilson E Motter,et al. Range-based attack on links in scale-free networks: are long-range links responsible for the small-world phenomenon? , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[36] D. Pe’er,et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.
[37] John R. Koza,et al. Reverse Engineering of Metabolic Pathways from Observed Data Using Genetic Programming , 2000, Pacific Symposium on Biocomputing.
[38] 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.
[39] Hidde de Jong,et al. Qualitative Simulation of Genetic Regulatory Networks: Method and Application , 2001, IJCAI.
[40] S. Fields,et al. Protein analysis on a proteomic scale , 2003, Nature.
[41] G. W. Hatfield,et al. DNA microarrays and gene expression , 2002 .
[42] Martin Stetter,et al. Exploration of Cortical Function , 2002, Springer Netherlands.
[43] J. Downing,et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. , 2002, Cancer cell.
[44] Ting Chen,et al. Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.
[45] 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.
[46] E. Davidson,et al. Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. , 1998, Science.
[47] D S Latchman,et al. Eukaryotic transcription factors. , 1990, The Biochemical journal.
[48] Satoru Miyano,et al. Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks , 2004, J. Bioinform. Comput. Biol..
[49] Satoru Miyano,et al. Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression , 2001, Pacific Symposium on Biocomputing.
[50] Werner Dubitzky,et al. Multiclass Cancer Classification Using Gene Expression Profiling and Probabilistic Neural Networks , 2002, Pacific Symposium on Biocomputing.
[51] S. Kauffman. Gene regulation networks: a theory for their global structure and behaviors. , 1971, Current topics in developmental biology.
[52] P. D’haeseleer,et al. Mining the gene expression matrix: inferring gene relationships from large scale gene expression data , 1998 .
[53] V. Bohr,et al. DNA damage and its processing. Relation to human disease , 2002, Journal of Inherited Metabolic Disease.
[54] Richard E. Korf,et al. Learning bayesian networks from data , 1996 .
[55] K D Robertson,et al. DNA methylation: past, present and future directions. , 2000, Carcinogenesis.
[56] S. Kauffman. Homeostasis and Differentiation in Random Genetic Control Networks , 1969, Nature.
[57] A. M. Turing,et al. The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.
[58] Dennis Shasha,et al. Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications , 1999 .
[59] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[60] Mark Schena,et al. Microarray Biochip Technology , 2000 .
[61] H. Lodish. Molecular Cell Biology , 1986 .
[62] J. Hopfield,et al. From molecular to modular cell biology , 1999, Nature.
[63] Anton Schwaighofer,et al. Mining functional modules in genetic networks with decomposable graphical models. , 2004, Omics : a journal of integrative biology.
[64] J. Yates. Mass spectrometry and the age of the proteome. , 1998, Journal of mass spectrometry : JMS.
[65] Fabian Model,et al. Feature selection for DNA methylation based cancer classification , 2001, ISMB.