Gene Regulatory Networks

Gene regulatory networks: methods and protocols , Gene regulatory networks: methods and protocols , کتابخانه‌های دانشگاه کردستان

[1]  David Haussler,et al.  Human Genome Ultraconserved Elements Are Ultraselected , 2007, Science.

[2]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[3]  D. Arnosti,et al.  Information display by transcriptional enhancers , 2003, Development.

[4]  Xiaoqian Sun,et al.  Bayes factor consistency for nested linear models with a growing number of parameters , 2014 .

[5]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[6]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[7]  David M. Rocke,et al.  A Model for Measurement Error for Gene Expression Arrays , 2001, J. Comput. Biol..

[8]  Robert Castelo,et al.  A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n , 2006, J. Mach. Learn. Res..

[9]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[10]  Tom Michoel,et al.  Integrative Multi-omics Module Network Inference with Lemon-Tree , 2014, PLoS Comput. Biol..

[11]  Jun Ma.  Transcriptional activators and activation mechanisms , 2011, Protein & Cell.

[12]  Mark K Transtrum,et al.  Geometry of nonlinear least squares with applications to sloppy models and optimization. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Christophe Ambroise,et al.  SIMoNe: Statistical Inference for MOdular NEtworks , 2009, Bioinform..

[14]  Clifford A. Meyer,et al.  Model-based Analysis of ChIP-Seq (MACS) , 2008, Genome Biology.

[15]  C. Giraud.  Estimation of Gaussian graphs by model selection , 2007, 0710.2044.

[16]  M. Levine,et al.  Regulation of two pair-rule stripes by a single enhancer in the Drosophila embryo. , 1996, Developmental biology.

[17]  D. Moazed,et al.  RNA-mediated epigenetic regulation of gene expression , 2015, Nature Reviews Genetics.

[18]  E. Davidson,et al.  The hardwiring of development: organization and function of genomic regulatory systems. , 1997, Development.

[19]  Mickael Guedj,et al.  Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions , 2014 .

[20]  Ryan N. Gutenkunst,et al.  Sloppiness and the Geometry of Parameter Space , 2016 .

[21]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[22]  Masaru Tomita,et al.  E-CELL: software environment for whole-cell simulation , 1999, Bioinform..

[23]  B. Hamkalo,et al.  Electron microscopic analysis of RNA transcription in preimplantation rabbit embryos , 2005, Chromosoma.

[24]  D. Haussler,et al.  Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. , 2005, Genome research.

[25]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[26]  K. Morris,et al.  The rise of regulatory RNA , 2014, Nature Reviews Genetics.

[27]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[28]  Julio R. Banga,et al.  Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems , 2013, BMC Systems Biology.

[29]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[30]  Trevor J. Hastie,et al.  The Graphical Lasso: New Insights and Alternatives , 2011, Electronic journal of statistics.

[31]  Jean-Philippe Vert,et al.  TIGRESS: Trustful Inference of Gene REgulation using Stability Selection , 2012, BMC Systems Biology.

[32]  Roded Sharan,et al.  ANAT 2.0: reconstructing functional protein subnetworks , 2017, BMC Bioinformatics.

[33]  M. Bernhard.  Introduction to Chaotic Dynamical Systems , 1992 .

[34]  M. Ptashne,et al.  Genes and Signals , 2001 .

[35]  Mathias Drton,et al.  A SINful approach to Gaussian graphical model selection , 2005 .

[36]  Kathleen Marchal,et al.  SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms , 2006, BMC Bioinformatics.

[37]  Roded Sharan,et al.  SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments , 2007, ISMB/ECCB.

[38]  Wasserman,et al.  Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.

[39]  Hideyuki Suzuki,et al.  Characterizing global evolutions of complex systems via intermediate network representations , 2012, Scientific Reports.

[40]  Ursula Klingmüller,et al.  Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood , 2009, Bioinform..

[41]  P. V. von Hippel,et al.  Non-specific DNA binding of genome regulating proteins as a biological control mechanism: I. The lac operon: equilibrium aspects. , 1974, Proceedings of the National Academy of Sciences of the United States of America.

[42]  P. Zhao,et al.  A path following algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE) , 2008, 0807.3734.

[43]  Eric H Davidson,et al.  Modeling the dynamics of transcriptional gene regulatory networks for animal development. , 2009, Developmental biology.

[44]  Jingchun Zhu,et al.  Realizing the Promise of Reverse Phase Protein Arrays for Clinical, Translational, and Basic Research: A Workshop Report , 2014, Molecular & Cellular Proteomics.

[45]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[46]  Xiao-Li Meng,et al.  SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .

[47]  E. Davidson,et al.  Quantitative developmental transcriptomes of the sea urchin Strongylocentrotus purpuratus. , 2014, Developmental biology.

[48]  Eric H Davidson,et al.  Visualization, documentation, analysis, and communication of large-scale gene regulatory networks. , 2009, Biochimica et biophysica acta.

[49]  Sorin Istrail,et al.  Logic Functions of the Genomic Cis-regulatory Code , 2005, UC.

[50]  D. Goldfarb.  A family of variable-metric methods derived by variational means , 1970 .

[51]  Jeffrey T Leek,et al.  Reproducible RNA-seq analysis using recount2 , 2017, Nature Biotechnology.

[52]  T. Mikkelsen,et al.  Rapid dissection and model-based optimization of inducible enhancers in human cells using a massively parallel reporter assay , 2012, Nature Biotechnology.

[53]  A. Bakken,et al.  A quantitative electron microscopic analysis of transcription in sea urchin embryos , 1979, Chromosoma.

[54]  M. Yuan,et al.  Model selection and estimation in the Gaussian graphical model , 2007 .

[55]  Pradeep Ravikumar,et al.  QUIC: quadratic approximation for sparse inverse covariance estimation , 2014, J. Mach. Learn. Res..

[56]  C. Giraud,et al.  Graph Selection with GGMselect , 2009, Statistical applications in genetics and molecular biology.

[57]  J. Nocedal.  Updating Quasi-Newton Matrices With Limited Storage , 1980 .

[58]  M. A. Shea,et al.  Free energy coupling within macromolecules. The chemical work of ligand binding at the individual sites in co-operative systems. , 1983, Journal of molecular biology.

[59]  Limsoon Wong,et al.  Why Batch Effects Matter in Omics Data, and How to Avoid Them. , 2017, Trends in biotechnology.

[60]  Griffin M. Weber,et al.  BioNumbers—the database of key numbers in molecular and cell biology , 2009, Nucleic Acids Res..

[61]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[62]  Larry A. Wasserman,et al.  Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models , 2010, NIPS.

[63]  I. Daubechies,et al.  Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.

[64]  Martha L. Bulyk,et al.  UniPROBE, update 2011: expanded content and search tools in the online database of protein-binding microarray data on protein–DNA interactions , 2010, Nucleic Acids Res..

[65]  Ali Shojaie,et al.  Selection and estimation for mixed graphical models. , 2013, Biometrika.

[66]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

[67]  Christophe Ambroise,et al.  Inferring sparse Gaussian graphical models with latent structure , 2008, 0810.3177.

[68]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[69]  Jianfeng Gao,et al.  Scalable training of L1-regularized log-linear models , 2007, ICML '07.

[70]  Arunima Chaudhuri,et al.  Cell Biology by the Numbers , 2016, The Yale Journal of Biology and Medicine.

[71]  V. Vassiliadis,et al.  Second-order sensitivities of general dynamic systems with application to optimal control problems , 1999 .

[72]  Edward R. Dougherty,et al.  Validation of gene regulatory networks: scientific and inferential , 2011, Briefings Bioinform..

[73]  S. Shen-Orr,et al.  Network motifs in the transcriptional regulation network of Escherichia coli , 2002, Nature Genetics.

[74]  J. Rousseau,et al.  A note on Bayes factor consistency in partial linear models , 2015 .

[75]  F. Feroz,et al.  MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics , 2008, 0809.3437.

[76]  R. Shibata.  An optimal selection of regression variables , 1981 .

[77]  R. Myers,et al.  CTCF/cohesin-mediated DNA looping is required for protocadherin α promoter choice , 2012, Proceedings of the National Academy of Sciences.

[78]  Barak A. Cohen,et al.  Complex effects of nucleotide variants in a mammalian cis-regulatory element , 2012, Proceedings of the National Academy of Sciences.

[79]  John J. Tyson,et al.  The Dynamics of Feedback Control Circuits in Biochemical Pathways , 1978 .

[80]  William Stafford Noble,et al.  FIMO: scanning for occurrences of a given motif , 2011, Bioinform..

[81]  Frank Emmert-Streib,et al.  Bagging Statistical Network Inference from Large-Scale Gene Expression Data , 2012, PloS one.

[82]  Tom Heskes,et al.  BCM: toolkit for Bayesian analysis of Computational Models using samplers , 2016, BMC Systems Biology.

[83]  M. Groudine,et al.  Proximity among distant regulatory elements at the beta-globin locus requires GATA-1 and FOG-1. , 2005, Molecular cell.

[84]  E. Davidson,et al.  Transcriptional regulatory cascades in development: Initial rates, not steady state, determine network kinetics , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[85]  S. Im,et al.  Lymphoid Enhancer Binding Factor 1 Regulates Transcription through Gene Looping1 , 2009, The Journal of Immunology.

[86]  R. Britten,et al.  Repetitive and Non-Repetitive DNA Sequences and a Speculation on the Origins of Evolutionary Novelty , 1971, The Quarterly Review of Biology.

[87]  Isabelle S. Peter,et al.  Transphyletic conservation of developmental regulatory state in animal evolution , 2011, Proceedings of the National Academy of Sciences.

[88]  R. Roeder,et al.  Direct interactions of OCA-B and TFII-I regulate immunoglobulin heavy-chain gene transcription by facilitating enhancer-promoter communication. , 2011, Molecular cell.

[89]  A. Mirsky.  The Chemistry of Heredity , 1953 .

[90]  Julien Mairal,et al.  Optimization with Sparsity-Inducing Penalties , 2011, Found. Trends Mach. Learn..

[91]  David N Arnosti,et al.  Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards? , 2005, Journal of cellular biochemistry.

[92]  David B. Berry,et al.  Pathway connectivity and signaling coordination in the yeast stress-activated signaling network , 2014, Molecular systems biology.

[93]  Michael A. West,et al.  Archival Version including Appendicies : Experiments in Stochastic Computation for High-Dimensional Graphical Models , 2005 .

[94]  E. Gusmão,et al.  Analysis of computational footprinting methods for DNase sequencing experiments , 2016, Nature Methods.

[95]  Mark A. Girolami,et al.  BioBayes: A software package for Bayesian inference in systems biology , 2008, Bioinform..

[96]  Rina Foygel,et al.  Extended Bayesian Information Criteria for Gaussian Graphical Models , 2010, NIPS.

[97]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[98]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[99]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[100]  Eric P. Xing,et al.  Graph estimation from multi-attribute data , 2012, J. Mach. Learn. Res..

[101]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[102]  AnHai Doan,et al.  MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive , 2017, Bioinform..

[103]  G. Stormo,et al.  Quantitative analysis demonstrates most transcription factors require only simple models of specificity , 2011, Nature Biotechnology.

[104]  M. Drton,et al.  Multiple Testing and Error Control in Gaussian Graphical Model Selection , 2005, math/0508267.

[105]  Theodor Boveri.  Ueber die Abhängigkeit der Kerngröße und Zellenzahl der Seeigel-Larven von der Chromosomenzahl der Ausgangszellen , 1905 .

[106]  E. Davidson,et al.  Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. , 1998, Science.

[107]  Nigel Duffy,et al.  L-SR1: A Second Order Optimization Method for Deep Learning , 2017 .

[108]  David J. Arenillas,et al.  JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles , 2015, Nucleic Acids Res..

[109]  P. Holland,et al.  Robust regression using iteratively reweighted least-squares , 1977 .

[110]  M C Mackey,et al.  Dynamic regulation of the tryptophan operon: a modeling study and comparison with experimental data. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[111]  Roded Sharan,et al.  Optimally Orienting Physical Networks , 2011, RECOMB.

[112]  Peng Zhao,et al.  On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..

[113]  Claire Donnat,et al.  Tracking Network Dynamics: a review of distances and similarity metrics , 2018, ArXiv.

[114]  A. West,et al.  The Protein CTCF Is Required for the Enhancer Blocking Activity of Vertebrate Insulators , 1999, Cell.

[115]  S. Barolo,et al.  Structural rules and complex regulatory circuitry constrain expression of a Notch- and EGFR-regulated eye enhancer. , 2010, Developmental cell.

[116]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[117]  Tanya Vavouri,et al.  Tuning in to the signals: noncoding sequence conservation in vertebrate genomes. , 2008, Trends in genetics : TIG.

[118]  Harri T. Kiiveri,et al.  Multivariate analysis of microarray data: differential expression and differential connection , 2011, BMC Bioinformatics.

[119]  Michael B. Eisen,et al.  A Careful Look at Binding Site Reorganization in the even-skipped Enhancers of Drosophila and Sepsids , 2008, PLoS genetics.

[120]  Derya B. Özyurt,et al.  Cheap Second Order Directional Derivatives of Stiff ODE Embedded Functionals , 2005, SIAM J. Sci. Comput..

[121]  J. Gurdon,et al.  “Fertile” Intestine Nuclei , 1966, Nature.

[122]  Roberto Visintainer,et al.  Distances and Stability in Biological Network Theory , 2013 .

[123]  Gang Li,et al.  Efficient Regularized Regression with L 0 Penalty for Variable Selection and Network Construction , 2016, Comput. Math. Methods Medicine.

[124]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[125]  Joseph B Hiatt,et al.  Massively parallel functional dissection of mammalian enhancers in vivo , 2012, Nature Biotechnology.

[126]  J. N. R. Jeffers,et al.  Graphical Models in Applied Multivariate Statistics. , 1990 .

[127]  Robin J. Wilson,et al.  An Atlas of Graphs , 1999 .

[128]  T. Cai,et al.  A Constrained ℓ1 Minimization Approach to Sparse Precision Matrix Estimation , 2011, 1102.2233.

[129]  C. D. Thron.  The secant condition for instability in biochemical feedback control—II. Models with upper Hessenberg Jacobian matrices , 1991 .

[130]  Alessandro Giuliani,et al.  Metabolic pathways variability and sequence/networks comparisons , 2006, BMC Bioinformatics.

[131]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[132]  M. West,et al.  Sparse graphical models for exploring gene expression data , 2004 .

[133]  V. Corces,et al.  Enhancer function: new insights into the regulation of tissue-specific gene expression , 2011, Nature Reviews Genetics.

[134]  P. Kokotovic,et al.  Direct and adjoint sensitivity equations for parameter optimization , 1967, IEEE Transactions on Automatic Control.

[135]  Alexander S Mikhailov,et al.  Evolutionary reconstruction of networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[136]  Jiahua Chen,et al.  Extended Bayesian information criteria for model selection with large model spaces , 2008 .

[137]  Venky N. Iyer,et al.  Sepsid even-skipped Enhancers Are Functionally Conserved in Drosophila Despite Lack of Sequence Conservation , 2008, PLoS genetics.

[138]  Mark Craven,et al.  Inferring Host Gene Subnetworks Involved in Viral Replication , 2014, PLoS Comput. Biol..

[139]  S. S. Wilks.  The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , 1938 .

[140]  F. Grosveld,et al.  The active spatial organization of the beta-globin locus requires the transcription factor EKLF. , 2004, Genes & development.

[141]  Hal L. Smith,et al.  Oscillations and multiple steady states in a cyclic gene model with repression , 1987, Journal of mathematical biology.

[142]  Fabian J Theis,et al.  Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines , 2017, bioRxiv.

[143]  Sylvie Huet,et al.  High-dimensional regression with unknown variance , 2011, 1109.5587.

[144]  A. Bakken,et al.  Morphological studies of transcription. , 1972, Acta endocrinologica. Supplementum.

[145]  J. Lafferty,et al.  High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.

[146]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[147]  R. Britten,et al.  Developmental appearance of factors that bind specifically to cis-regulatory sequences of a gene expressed in the sea urchin embryo. , 1988, Genes & development.

[148]  V. Corces,et al.  Insulators, long-range interactions, and genome function. , 2012, Current opinion in genetics & development.

[149]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[150]  J. Brachet.  L'hypothèse des plasmagènes dans le développement et la différenciation , 1949 .

[151]  Yoshua Bengio,et al.  Input-output HMMs for sequence processing , 1996, IEEE Trans. Neural Networks.

[152]  Alexandre d'Aspremont,et al.  Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .

[153]  J. Friedman,et al.  New Insights and Faster Computations for the Graphical Lasso , 2011 .

[154]  A. Morillon,et al.  Noncoding RNAs in gene regulation , 2011, Wiley interdisciplinary reviews. Systems biology and medicine.

[155]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[156]  Ann Dean,et al.  Enhancer and promoter interactions-long distance calls. , 2012, Current opinion in genetics & development.

[157]  J. Mallet-Paret,et al.  The Poincare-Bendixson theorem for monotone cyclic feedback systems , 1990 .

[158]  Eric H Davidson,et al.  A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo. , 2002, Developmental biology.

[159]  Stéphane Robin,et al.  Bayesian Inference of Graphical Model Structures Using Trees , 2015 .

[160]  Ernest Fraenkel,et al.  Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package , 2016, PLoS Comput. Biol..

[161]  R. Shibata.  Asymptotically Efficient Selection of the Order of the Model for Estimating Parameters of a Linear Process , 1980 .

[162]  Giancarlo Mauri,et al.  LASSIE: simulating large-scale models of biochemical systems on GPUs , 2017, BMC Bioinformatics.

[163]  A. Califano,et al.  Dialogue on Reverse‐Engineering Assessment and Methods , 2007, Annals of the New York Academy of Sciences.

[164]  M. Girolami,et al.  Riemann manifold Langevin and Hamiltonian Monte Carlo methods , 2011, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[165]  N. Meinshausen,et al.  Stability selection , 2008, 0809.2932.

[166]  G. Stormo,et al.  A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity , 2011, Nucleic acids research.

[167]  James Demmel,et al.  An Asynchronous Parallel Supernodal Algorithm for Sparse Gaussian Elimination , 1997, SIAM J. Matrix Anal. Appl..

[168]  Roger Fletcher,et al.  A Rapidly Convergent Descent Method for Minimization , 1963, Comput. J..

[169]  P. Bühlmann,et al.  Statistical Applications in Genetics and Molecular Biology Low-Order Conditional Independence Graphs for Inferring Genetic Networks , 2011 .

[170]  David Henriques,et al.  Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach , 2015, Bioinform..

[171]  Ruth Nussinov,et al.  Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.

[172]  Eric H Davidson,et al.  High accuracy, high-resolution prevalence measurement for the majority of locally expressed regulatory genes in early sea urchin development. , 2010, Gene expression patterns : GEP.

[173]  William J. R. Longabaugh,et al.  Computational representation of developmental genetic regulatory networks. , 2005, Developmental biology.

[174]  P. Rigby,et al.  Analysis of a key regulatory region upstream of the Myf5 gene reveals multiple phases of myogenesis, orchestrated at each site by a combination of elements dispersed throughout the locus , 2003, Development.

[175]  G. Dressler,et al.  Role of PTIP in Class Switch Recombination and Long-Range Chromatin Interactions at the Immunoglobulin Heavy Chain Locus , 2011, Molecular and Cellular Biology.

[176]  Ge Gao,et al.  PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants , 2016, Nucleic Acids Res..

[177]  C. A. Kofoid.  The Cell in Development and Heredity , 1926 .

[178]  Cesare Furlanello,et al.  Tumor-infiltrating T lymphocytes improve clinical outcome of therapy-resistant neuroblastoma , 2015, Oncoimmunology.

[179]  C. D. Lewis,et al.  Interaction of specific nuclear factors with the nuclease-hypersensitive region of the chicken adult β-globin gene: Nature of the binding domain , 1985, Cell.

[180]  G. Stormo,et al.  Determining the specificity of protein–DNA interactions , 2010, Nature Reviews Genetics.

[181]  Kenneth N. Brown,et al.  Graph metrics as summary statistics for Approximate Bayesian Computation with application to network model parameter estimation , 2015, J. Complex Networks.

[182]  Shiming Chen,et al.  Active opsin loci adopt intrachromosomal loops that depend on the photoreceptor transcription factor network , 2011, Proceedings of the National Academy of Sciences.

[183]  Kate B. Cook,et al.  Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity , 2014, Cell.

[184]  Dimitris Thanos,et al.  Transcription factors mediate long-range enhancer–promoter interactions , 2009, Proceedings of the National Academy of Sciences.

[185]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[186]  B. Kaltenbacher,et al.  A refinement and coarsening indicator algorithm for finding sparse solutions of inverse problems , 2011 .

[187]  E. Stedman,et al.  Cell Specificity of Histones , 1950, Nature.

[188]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[189]  Linda R Petzold,et al.  The slow-scale stochastic simulation algorithm. , 2005, The Journal of chemical physics.

[190]  Cesare Furlanello,et al.  Sparse Predictive Structure of Deconvolved Functional Brain Networks , 2013, 1310.6547.

[191]  E. Davidson,et al.  Global regulatory logic for specification of an embryonic cell lineage , 2008, Proceedings of the National Academy of Sciences.

[192]  A. Stathopoulos,et al.  Design flexibility in cis-regulatory control of gene expression: synthetic and comparative evidence. , 2009, Developmental biology.

[193]  Andrea Rau,et al.  Reverse engineering gene regulatory networks using approximate Bayesian computation , 2012, Stat. Comput..

[194]  Zachary D. Smith,et al.  Unbiased Reconstruction of a Mammalian Transcriptional Network Mediating Pathogen Responses , 2009 .

[195]  Judith Marsman,et al.  Long distance relationships: enhancer-promoter communication and dynamic gene transcription. , 2012, Biochimica et biophysica acta.

[196]  三代 剛.  Architectural roles of multiple chromatin insulators at the human apolipoprotein gene cluster , 2009 .

[197]  M. Berger,et al.  Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors , 2009, Nature Protocols.

[198]  Jan Hasenauer,et al.  Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes. , 2017, Methods in molecular biology.

[199]  J. Kuha.  AIC and BIC , 2004 .

[200]  T. Morgan.  Embryology And Genetics , 1934 .

[201]  Bin Yu,et al.  High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence , 2008, 0811.3628.

[202]  David M. Simcha,et al.  Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.

[203]  Isabelle S. Peter,et al.  Predictive computation of genomic logic processing functions in embryonic development , 2012, Proceedings of the National Academy of Sciences.

[204]  Krzysztof Fujarewicz,et al.  On fitting of mathematical models of cell signaling pathways using adjoint systems. , 2005, Mathematical biosciences and engineering : MBE.

[205]  Eva Balsa-Canto,et al.  Dynamic optimization of chemical and biochemical processes using restricted second order information , 2001 .

[206]  Eric H. Davidson,et al.  Transcriptional Network Logic: The Systems Biology of Development , 2013 .

[207]  Joël Janin,et al.  Physical biology of the cell, Second Edition , 2013 .

[208]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[209]  Klaudia Walter,et al.  Highly Conserved Non-Coding Sequences Are Associated with Vertebrate Development , 2004, PLoS biology.

[210]  T. Maniatis,et al.  Virus induction of human IFNβ gene expression requires the assembly of an enhanceosome , 1995, Cell.

[211]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .

[212]  Samantha A. Morris,et al.  CellNet: Network Biology Applied to Stem Cell Engineering , 2014, Cell.

[213]  Syr-yaung Lin,et al.  The general affinity of lac repressor for E. coli DNA: Implications for gene regulation in procaryotes and eucaryotes , 1975, Cell.

[214]  F. Giancotti.  Deregulation of cell signaling in cancer , 2014, FEBS letters.

[215]  E. N.,et al.  The Calculus of Finite Differences , 1934, Nature.

[216]  John Quackenbush,et al.  Multiple-laboratory comparison of microarray platforms , 2005, Nature Methods.

[217]  S. Bekiranov,et al.  Measuring Chromatin Interaction Dynamics on the Second Time Scale at Single-Copy Genes , 2013, Science.

[218]  H. Woodland.  Gene activity in early development. Third edition By E. H. Davidson. Orlando, Florida: Academic Press. (1986). 670 pp. $49.50 , 1987, Cell.

[219]  Robert J. Gelinas,et al.  Sensitivity analysis of ordinary differential equation systems—A direct method , 1976 .

[220]  Stefanie Widder,et al.  A generalized model of the repressilator , 2006, Journal of mathematical biology.

[221]  E. Davidson,et al.  Cis-regulatory logic in the endo16 gene: switching from a specification to a differentiation mode of control. , 2001, Development.

[222]  Natallia Katenka,et al.  Inference and Characterization of Multi-Attribute Networks with Application to Computational Biology , 2011, 1109.3160.

[223]  M. Levine,et al.  Regulation of a segmentation stripe by overlapping activators and repressors in the Drosophila embryo. , 1991, Science.

[224]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[225]  Sylvie Huet,et al.  Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems , 2008, Statistical applications in genetics and molecular biology.

[226]  J. Gurdon,et al.  Genetic Content of Adult Somatic Cells tested by Nuclear Transplantation from Cultured Cells , 1970, Nature.

[227]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[228]  Francis R. Bach,et al.  Bolasso: model consistent Lasso estimation through the bootstrap , 2008, ICML '08.

[229]  Esther Bae,et al.  Disruption of the Abdominal-B Promoter Tethering Element Results in a Loss of Long-Range Enhancer-Directed Hox Gene Expression in Drosophila , 2011, PloS one.

[230]  W. Reinhold,et al.  Topoisomerase I levels in the NCI-60 cancer cell line panel determined by validated ELISA and microarray analysis and correlation with indenoisoquinoline sensitivity , 2009, Molecular Cancer Therapeutics.

[231]  Hal L. Smith.  Systems of ordinary differential equations which generate an order preserving flow. A survey of results , 1988 .

[232]  E. Davidson,et al.  Spatial and temporal information processing in the sea urchin embryo: modular and intramodular organization of the CyIIIa gene cis-regulatory system. , 1996, Development.

[233]  M. Laubichler,et al.  Boveri's long experiment: sea urchin merogones and the establishment of the role of nuclear chromosomes in development. , 2008, Developmental biology.

[234]  T. M. Sonneborn.  The cytoplasm in heredity1 , 1950, Heredity.

[235]  William Stafford Noble,et al.  How does multiple testing correction work? , 2009, Nature Biotechnology.

[236]  J. Navarro-Pedreño.  Numerical Methods for Least Squares Problems , 1996 .

[237]  Tamer Kahveci,et al.  Accessed Terms of Use , 2022 .

[238]  Eric H. Davidson,et al.  A gene regulatory network controlling the embryonic specification of endoderm , 2011, Nature.

[239]  E. Davidson.  The Regulatory Genome: Gene Regulatory Networks In Development And Evolution , 2006 .

[240]  E. Furlong,et al.  Transcription factors: from enhancer binding to developmental control , 2012, Nature Reviews Genetics.

[241]  R. Britten,et al.  Gene regulation for higher cells: a theory. , 1969, Science.

[242]  Ariel S. Schwartz,et al.  An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man , 2010, Cell.

[243]  Richard H. Byrd,et al.  Analysis of a Symmetric Rank-One Trust Region Method , 1996, SIAM J. Optim..

[244]  Cesare Furlanello,et al.  Algebraic Comparison of Partial Lists in Bioinformatics , 2010, PloS one.

[245]  G. N. Lance,et al.  Computer Programs for Hierarchical Polythetic Classification ("Similarity Analyses") , 1966, Comput. J..

[246]  Christophe Ambroise,et al.  Inferring Multiple Graphical Models , 2009 .

[247]  Zonghao Gu,et al.  Generating Multiple Solutions for Mixed Integer Programming Problems , 2007, IPCO.