Graphical models via generalized linear models

Advances in Neural Information Processing Systems

Volumn 2, Issue , 2012, Pages 1358-1366

  Yang, Eunho ^a   Ravikumar, Pradeep ^a   Allen, Genevera I ^b   Liu, Zhandong ^c  

^a University of Texas at Austin   (United States)

^b Rice University   (United States)

^c BAYLOR COLLEGE OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL DISTRIBUTION; DISCRETE MODELS; EXPONENTIAL DISTRIBUTIONS; EXPONENTIAL FAMILY; GAUSSIAN MARKOV RANDOM FIELD; GENERALIZED LINEAR MODEL; HIGH PROBABILITY; NEGATIVE BINOMIAL;

GAUSSIAN NOISE (ELECTRONIC); ISING MODEL; MARKOV PROCESSES; POISSON DISTRIBUTION;

GRAPHIC METHODS;

EID: 84877773753     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. M.J. Wainwright and M.I. Jordan. Graphical models, exponential families, and variational inference. Foundations and TrendsR in Machine Learning, 1(1-2):1-305, 2008.
2. N. Meinshausen and P. Bühlmann. High-dimensional graphs and variable selection with the Lasso. Annals of Statistics, 34:1436-1462, 2006.
3. 1-regularized logistic regression. Annals of Statistics, 38(3):1287-1319, 2010.
4. A. Jalali, P. Ravikumar, V. Vasuki, and S. Sanghavi. On learning discrete graphical models using group-sparse regularization. In Inter. Conf. on AI and Statistics (AISTATS), 14, 2011.
5. P. McCullagh and J.A. Nelder. Generalized linear models. Monographs on statistics and applied probability 37. Chapman and Hall/CRC, New York, 1989.
6. S.L. Lauritzen. Graphical models, volume 17. Oxford University Press, USA, 1996.
7. J. Besag. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological), 36(2):192-236, 1974.
8. H. Liu, J. Lafferty, and L. Wasserman. The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. The Journal of Machine Learning Research, 10:2295-2328, 2009.
9. Y.M.M. Bishop, S.E. Fienberg, and P.W. Holland. Discrete multivariate analysis. Springer Verlag, 2007.
10. Trevor. Hastie, Robert. Tibshirani, and JH (Jerome H.) Friedman. The elements of statistical learning. Springer, 2 edition, 2009.
11. J.C. Marioni, C.E. Mason, S.M. Mane, M. Stephens, and Y. Gilad. Rna-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research, 18(9):1509-1517, 2008.
12. S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu. A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers, 2010.
13. Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature, 490(7418):61-70, 2012.
14. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216):1061-1068, October 2008.
15. H. Liu, K. Roeder, and L. Wasserman. Stability approach to regularization selection (stars) for high dimensional graphical models. Arxiv preprint arXiv:1006.3316, 2010.
16. K. Abdelmohsen, M.M. Kim, S. Srikantan, E.M. Mercken, S.E. Brennan, G.M. Wilson, R. de Cabo, and M. Gorospe. mir-519 suppresses tumor growth by reducing hur levels. Cell cycle (Georgetown, Tex.), 9(7):1354, 2010.
17. I. Keklikoglou, C. Koerner, C. Schmidt, JD Zhang, D. Heckmann, A. Shavinskaya, H. Allgayer, B. Gückel, T. Fehm, A. Schneeweiss, 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. Oncogene, 2011.
18. F. Yu, H. Yao, P. Zhu, X. Zhang, Q. Pan, C. Gong, Y. Huang, X. Hu, F. Su, J. Lieberman, et al. let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell, 131(6):1109-1123, 2007.
19. R. McLendon, A. Friedman, D. Bigner, E.G. Van Meir, D.J. Brat, G.M. Mastrogianakis, J.J. Olson, T. Mikkelsen, N. Lehman, K. Aldape, et al. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455(7216):1061-1068, 2008.
20. Jianhua Zhang. Convert segment data into a region by sample matrix to allow for other high level computational analyses, version 1.2.0 edition. Bioconductor package.
21. Gerald B W Wertheim, Thomas W Yang, Tien-chi Pan, Anna Ramne, Zhandong Liu, Heather P Gardner, Katherine D Dugan, Petra Kristel, Bas Kreike, Marc J van de Vijver, Robert D Cardiff, Carol Reynolds, and Lewis A Chodosh. The Snf1-related kinase, Hunk, is essential for mammary tumor metastasis. Proceedings of the National Academy of Sciences of the United States of America, 106(37):15855-15860, September 2009.
22. J.T. Leek, R.B. Scharpf, H.C. Bravo, D. Simcha, B. Langmead, W.E. Johnson, D. Geman, K. Baggerly, and R.A. Irizarry. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10):733-739, 2010.
23. J. Li, D.M. Witten, I.M. Johnstone, and R. Tibshirani. Normalization, testing, and false discovery rate estimation for rna-sequencing data. Biostatistics, 2011.
24. G. I. Allen and Z. Liu. A Log-Linear Graphical Model for Inferring Genetic Networks from High-Throughput Sequencing Data. IEEE International Conference on Bioinformatics and Biomedicine, 2012.
25. J. Bullard, E. Purdom, K. Hansen, and S. Dudoit. Evaluation of statistical methods for normalization and differential expression in mrna-seq experiments. BMC bioinformatics, 11(1):94, 2010.