Some Useful Mathematical Tools to Transform Microarray Data into Interactive Molecular Networks
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V. Anne Smith | Franziska Matthäus | Peter J. Gebicke-Haerter | F. Matthäus | V. Smith | P. Gebicke-haerter
[1] Christopher J Portier,et al. Gene interaction network analysis suggests differences between high and low doses of acetaminophen. , 2006, Toxicology and applied pharmacology.
[2] J. Sepulcre,et al. A Network Analysis of the Human T-Cell Activation Gene Network Identifies Jagged1 as a Therapeutic Target for Autoimmune Diseases , 2007, PloS one.
[3] D. Husmeier,et al. Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge , 2007, Statistical applications in genetics and molecular biology.
[4] Satoru Miyano,et al. Utilizing Evolutionary Information and Gene Expression Data for Estimating Gene Networks with Bayesian Network Models , 2005, J. Bioinform. Comput. Biol..
[5] John Quackenbush,et al. Seeded Bayesian Networks: Constructing genetic networks from microarray data , 2008, BMC Systems Biology.
[6] Satoru Miyano,et al. Combining Microarrays and Biological Knowledge for Estimating Gene Networks via Bayesian Networks , 2004, J. Bioinform. Comput. Biol..
[7] Nir Friedman,et al. Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks , 2004, Machine Learning.
[8] Eric E. Schadt,et al. Moving toward a system genetics view of disease , 2007, Mammalian Genome.
[9] Michael Q. Zhang,et al. Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells , 2008, BMC Bioinformatics.
[10] Xiaoyun Sun,et al. Computational modeling of Caenorhabditis elegans vulval induction , 2007, ISMB/ECCB.
[11] Vincent Frouin,et al. Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset , 2008, BMC Bioinformatics.
[12] Ralf Herwig,et al. GeNGe: systematic generation of gene regulatory networks , 2009, Bioinform..
[13] Amos Tanay,et al. Minreg: Inferring an active regulator set , 2002, ISMB.
[14] Tianzi Jiang,et al. Exploring candidate genes for human brain diseases from a brain-specific gene network. , 2006, Biochemical and biophysical research communications.
[15] Nir Friedman,et al. Inferring Cellular Networks Using Probabilistic Graphical Models , 2004, Science.
[16] D. Hand,et al. Finding Groups in Gene Expression Data , 2005, Journal of biomedicine & biotechnology.
[17] Satoru Miyano,et al. Inferring gene networks from time series microarray data using dynamic Bayesian networks , 2003, Briefings Bioinform..
[18] Isabel M. Tienda-Luna,et al. Uncovering Gene Regulatory Networks from Time-Series Microarray Data with Variational Bayesian Structural Expectation Maximization , 2007, EURASIP J. Bioinform. Syst. Biol..
[19] Xiaoyu Chen,et al. Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees , 2007, BMC Bioinformatics.
[20] Gregory F. Cooper,et al. A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.
[21] Christian J Stoeckert,et al. Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome-wide scale. , 2006, Genome research.
[22] Li Zhu,et al. Expression profiling analysis for genes related to meat quality and carcass traits during postnatal development of backfat in two pig breeds , 2008, Science in China Series C: Life Sciences.
[23] 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.
[24] S. Rodriguez-Zas,et al. Advancing the understanding of the embryo transcriptome co-regulation using meta-, functional, and gene network analysis tools. , 2008, Reproduction.
[25] Satoru Miyano,et al. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. , 2004 .
[26] Luis Mateus Rocha,et al. Singular value decomposition and principal component analysis , 2003 .
[27] David Maxwell Chickering,et al. Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..
[28] David Maxwell Chickering,et al. Learning Bayesian Networks is NP-Complete , 2016, AISTATS.
[29] Satoru Miyano,et al. Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection , 2003, ECCB.
[30] G. Churchill,et al. Statistical design and the analysis of gene expression microarray data. , 2007, Genetical research.
[31] Dirk Husmeier,et al. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..
[32] Rainer Spang,et al. Inferring cellular networks – a review , 2007, BMC Bioinformatics.
[33] R. Spanagel,et al. Transcriptional changes in insulin‐ and lipid metabolism‐related genes in the hippocampus of olfactory bulbectomized mice , 2008, Journal of neuroscience research.
[34] Alexander J. Hartemink,et al. Informative Structure Priors: Joint Learning of Dynamic Regulatory Networks from Multiple Types of Data , 2004, Pacific Symposium on Biocomputing.
[35] Chih-Hung Hsieh,et al. An Intelligent Two-Stage Evolutionary Algorithm for Dynamic Pathway Identification From Gene Expression Profiles , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[36] M. Stetter,et al. Hunting drug targets by systems-level modeling of gene expression profiles , 2004, IEEE Transactions on NanoBioscience.
[37] Satoru Miyano,et al. Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network , 2003, J. Bioinform. Comput. Biol..
[38] G. W. Milligan,et al. An examination of procedures for determining the number of clusters in a data set , 1985 .
[39] Peter J. Woolf,et al. Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information , 2008, BMC Bioinformatics.
[40] Nir Friedman,et al. Inferring subnetworks from perturbed expression profiles , 2001, ISMB.
[41] Min Zou,et al. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data , 2005, Bioinform..
[42] K. Ohyashiki,et al. Estimating immunoregulatory gene networks in human herpesvirus type 6-infected T cells. , 2005, Biochemical and biophysical research communications.
[43] David Page,et al. Modelling regulatory pathways in E. coli from time series expression profiles , 2002, ISMB.
[44] Mingyi Wang,et al. A hybrid Bayesian network learning method for constructing gene networks , 2007, Comput. Biol. Chem..
[45] S. Muta,et al. Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. , 2003, DNA research : an international journal for rapid publication of reports on genes and genomes.
[46] V. Anne Smith,et al. Evaluating functional network inference using simulations of complex biological systems , 2002, ISMB.
[47] Ding-Zhu Du,et al. A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering , 2003, J. Glob. Optim..
[48] Sascha Ott,et al. Increasing feasibility of optimal gene network estimation. , 2004, Genome informatics. International Conference on Genome Informatics.
[49] Paul P. Wang,et al. Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..
[50] F Matthäus,et al. Interactive Molecular Networks Obtained by Computer-aided Conversion of Microarray Data from Brains of Alcohol-drinking Rats , 2009, Pharmacopsychiatry.
[51] G. Gibson,et al. Analysis of variance of microarray data. , 2006, Methods in enzymology.
[52] Wai Lam,et al. LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..
[53] I. Simon,et al. Combined static and dynamic analysis for determining the quality of time-series expression profiles , 2005, Nature Biotechnology.
[54] P. Spirtes,et al. An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .
[55] Joe Suzuki,et al. A Construction of Bayesian Networks from Databases Based on an MDL Principle , 1993, UAI.
[56] Aurélien Mazurie,et al. Gene networks inference using dynamic Bayesian networks , 2003, ECCB.
[57] Bartek Wilczynski,et al. Applying dynamic Bayesian networks to perturbed gene expression data , 2006, BMC Bioinformatics.
[58] Doheon Lee,et al. Modularized learning of genetic interaction networks from biological annotations and mRNA expression data , 2005, Bioinform..
[59] T. Yamanaka,et al. Gene Interaction Network Suggests Dioxin Induces a Significant Linkage between Aryl Hydrocarbon Receptor and Retinoic Acid Receptor Beta , 2004, Environmental health perspectives.
[60] J. Derisi,et al. A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae , 2006, PloS one.
[61] Gary A. Churchill,et al. Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..
[62] David J. Spiegelhalter,et al. Bayesian analysis in expert systems , 1993 .
[63] Michal Linial,et al. Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..
[64] Jianhong Wu,et al. Data clustering - theory, algorithms, and applications , 2007 .
[65] Bart De Moor,et al. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.
[66] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.