Graphical models via univariate exponential family distributions
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Pradeep Ravikumar | Eunho Yang | Genevera I. Allen | Zhandong Liu | Pradeep Ravikumar | Eunho Yang | Zhandong Liu | Zhandong Liu
[1] M. Stephens,et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.
[2] Sandrine Dudoit,et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.
[3] Ali Jalali,et al. On Learning Discrete Graphical Models using Group-Sparse Regularization , 2011, AISTATS.
[4] B. Williams,et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.
[5] Martin A. Nowak,et al. Inferring Cellular Networks Using Probabilistic Graphical Models , 2004 .
[6] Larry A. Wasserman,et al. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models , 2010, NIPS.
[7] T. Speed,et al. Gaussian Markov Distributions over Finite Graphs , 1986 .
[8] A. Dobra,et al. Copula Gaussian graphical models and their application to modeling functional disability data , 2011, 1108.1680.
[9] Pradeep Ravikumar,et al. Graphical Models via Generalized Linear Models , 2012, NIPS.
[10] J. Lafferty,et al. High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.
[11] Martin J. Wainwright,et al. Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$ -Constrained Quadratic Programming (Lasso) , 2009, IEEE Transactions on Information Theory.
[12] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[13] Marc Teboulle,et al. Gradient-based algorithms with applications to signal-recovery problems , 2010, Convex Optimization in Signal Processing and Communications.
[14] L. J.,et al. Normalization , testing , and false discovery rate estimation for RNA-sequencing data , 2012 .
[15] I. Daubechies,et al. Accelerated Projected Gradient Method for Linear Inverse Problems with Sparsity Constraints , 2007, 0706.4297.
[16] Larry A. Wasserman,et al. Sparse Nonparametric Graphical Models , 2012, ArXiv.
[17] R. Tibshirani,et al. Monographs on statistics and applied probability , 1990 .
[18] B Peter. Statistics for High-Dimensional Data: Selected Topics , 2014 .
[19] Sara van de Geer,et al. Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .
[20] N. Reid,et al. AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS , 2011 .
[21] Hongzhe Li,et al. A Markov random field model for network-based analysis of genomic data , 2007, Bioinform..
[22] H. Zou,et al. Regularized rank-based estimation of high-dimensional nonparanormal graphical models , 2012, 1302.3082.
[23] Larry A. Wasserman,et al. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs , 2009, J. Mach. Learn. Res..
[24] David M. Simcha,et al. Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.
[25] Steven J. M. Jones,et al. Comprehensive molecular portraits of human breast tumors , 2012, Nature.
[26] P. McCullagh,et al. Generalized Linear Models , 1992 .
[27] Kotb Abdelmohsen,et al. miR-519 suppresses tumor growth by reducing HuR levels , 2010, Cell cycle.
[28] D. Acemoglu. THE CRISIS OF 2008: LESSONS FOR AND FROM ECONOMICS , 2009 .
[29] P. Holland,et al. Discrete Multivariate Analysis. , 1976 .
[30] Peter Clifford,et al. Markov Random Fields in Statistics , 2012 .
[31] Genevera I. Allen,et al. A Log-Linear Graphical Model for inferring genetic networks from high-throughput sequencing data , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.
[32] R. Tibshirani,et al. A SIGNIFICANCE TEST FOR THE LASSO. , 2013, Annals of statistics.
[33] Michael I. Jordan. Graphical Models , 1998 .
[34] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[35] G. Grimmett,et al. Disorder in physical systems : a volume in honour of John M. Hammersley on the occasion of his 70th birthday , 1990 .
[36] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[37] C. Varin,et al. A note on composite likelihood inference and model selection , 2005 .
[38] RavikumarPradeep,et al. Graphical models via univariate exponential family distributions , 2015 .
[39] Nir Friedman,et al. Inferring subnetworks from perturbed expression profiles , 2001, ISMB.
[40] K. Sachs,et al. Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.
[41] J. Lieberman,et al. let-7 Regulates Self Renewal and Tumorigenicity of Breast Cancer Cells , 2007, Cell.
[42] J. Besag. Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .
[43] R. Tibshirani. A signicance test for the lasso , 2014 .
[44] I. Keklikoglou,et al. MicroRNA-520/373 family functions as a tumor suppressor in estrogen receptor negative breast cancer by targeting NF-κB and TGF-β signaling pathways , 2012, Oncogene.
[45] Leonore A Herzenberg,et al. Interpreting flow cytometry data: a guide for the perplexed , 2006, Nature Immunology.
[46] Genevera I. Allen,et al. A Local Poisson Graphical Model for Inferring Networks From Sequencing Data , 2013, IEEE Transactions on NanoBioscience.
[47] Larry A. Wasserman,et al. High Dimensional Semiparametric Gaussian Copula Graphical Models. , 2012, ICML 2012.
[48] Steven J. M. Jones,et al. Comprehensive molecular portraits of human breast tumours , 2013 .
[49] Larry A. Wasserman,et al. The Nonparanormal SKEPTIC , 2012, ICML 2012.
[50] R. Tibshirani,et al. Sparse inverse covariance estimation with the lasso , 2007, 0708.3517.