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Sparse graphical models for exploring gene expression data

Citations

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Cited by:

  1. Villers Fanny & Schaeffer Brigitte & Bertin Caroline & Huet Sylvie, 2008. "Assessing the Validity Domains of Graphical Gaussian Models in Order to Infer Relationships among Components of Complex Biological Systems," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-37, September.
  2. Banerjee, Sayantan & Ghosal, Subhashis, 2015. "Bayesian structure learning in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 147-162.
  3. Agius Phaedra & Ying Yiming & Campbell Colin, 2009. "Bayesian Unsupervised Learning with Multiple Data Types," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
  4. Yiming Hu & Hongyu Zhao, 2016. "CCor: A whole genome network‐based similarity measure between two genes," Biometrics, The International Biometric Society, vol. 72(4), pages 1216-1225, December.
  5. Ashish Arora & Michelle Gittelman & Sarah Kaplan & John Lynch & Will Mitchell & Nicolaj Siggelkow & Mei Li & Ying Lin & Shuai Huang & Craig Crossland, 2016. "The use of sparse inverse covariance estimation for relationship detection and hypothesis generation in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 37(1), pages 86-97, January.
  6. Elin Shaddox & Francesco C. Stingo & Christine B. Peterson & Sean Jacobson & Charmion Cruickshank-Quinn & Katerina Kechris & Russell Bowler & Marina Vannucci, 2018. "A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 59-85, April.
  7. Zhixiang Lin & Tao Wang & Can Yang & Hongyu Zhao, 2017. "On joint estimation of Gaussian graphical models for spatial and temporal data," Biometrics, The International Biometric Society, vol. 73(3), pages 769-779, September.
  8. Donatello Telesca & Peter Müller & Steven M. Kornblau & Marc A. Suchard & Yuan Ji, 2012. "Modeling Protein Expression and Protein Signaling Pathways," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1372-1384, December.
  9. Chen, Shyh-Huei & Ip, Edward H. & Wang, Yuchung J., 2011. "Gibbs ensembles for nearly compatible and incompatible conditional models," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1760-1769, April.
  10. Xiaodong Cai & Juan Andrés Bazerque & Georgios B Giannakis, 2013. "Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-13, May.
  11. Chun, Hyonho & Lee, Myung Hee & Fleet, James C. & Oh, Ji Hwan, 2016. "Graphical models via joint quantile regression with component selection," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 162-171.
  12. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
  13. Giraud Christophe & Huet Sylvie & Verzelen Nicolas, 2012. "Graph Selection with GGMselect," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-52, February.
  14. Wang, Hao, 2010. "Sparse seemingly unrelated regression modelling: Applications in finance and econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2866-2877, November.
  15. Wessel N. van Wieringen & Carel F. W. Peeters & Renee X. de Menezes & Mark A. van de Wiel, 2018. "Testing for pathway (in)activation by using Gaussian graphical models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1419-1436, November.
  16. Aliaksandr Hubin & Geir Storvik, 2024. "Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference," Mathematics, MDPI, vol. 12(6), pages 1-28, March.
  17. Gómez-Villegas, M.A. & Main, P. & Navarro, H. & Susi, R., 2014. "Sensitivity to hyperprior parameters in Gaussian Bayesian networks," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 214-225.
  18. Jacobovic Royi, 2018. "On the relation between the true and sample correlations under Bayesian modelling of gene expression datasets," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(4), pages 1-14, August.
  19. Nikolaos Petrakis & Stefano Peluso & Dimitris Fouskakis & Guido Consonni, 2020. "Objective methods for graphical structural learning," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 420-438, August.
  20. Codazzi, Laura & Colombi, Alessandro & Gianella, Matteo & Argiento, Raffaele & Paci, Lucia & Pini, Alessia, 2022. "Gaussian graphical modeling for spectrometric data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
  21. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
  22. Laurenţiu Cătălin Hinoveanu & Fabrizio Leisen & Cristiano Villa, 2020. "A loss‐based prior for Gaussian graphical models," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 444-466, December.
  23. Kriti Puniyani & Eric P Xing, 2013. "GINI: From ISH Images to Gene Interaction Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-15, October.
  24. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
  25. Li, Peili & Xiao, Yunhai, 2018. "An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 292-307.
  26. Paci, Lucia & Consonni, Guido, 2020. "Structural learning of contemporaneous dependencies in graphical VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  27. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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