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A sparse ising model with covariates

dc.contributor.authorCheng, Jieen_US
dc.contributor.authorLevina, Elizavetaen_US
dc.contributor.authorWang, Peien_US
dc.contributor.authorZhu, Jien_US
dc.date.accessioned2015-01-07T15:22:42Z
dc.date.availableWITHHELD_12_MONTHSen_US
dc.date.available2015-01-07T15:22:42Z
dc.date.issued2014-12en_US
dc.identifier.citationCheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji (2014). "A sparse ising model with covariates." Biometrics 70(4): 943-953.en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/109784
dc.publisherWiley Periodicals, Inc.en_US
dc.subject.otherPseudo‐Likelihooden_US
dc.subject.otherTumor Suppressor Genesen_US
dc.subject.otherBinary Markov Networken_US
dc.subject.otherGraphical Modelen_US
dc.subject.otherIsing Modelen_US
dc.subject.otherLassoen_US
dc.titleA sparse ising model with covariatesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/109784/1/biom12202.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/109784/2/biom12202-sm-0001-SupData-S1.pdf
dc.identifier.doi10.1111/biom.12202en_US
dc.identifier.sourceBiometricsen_US
dc.identifier.citedreferenceRavikumar, P., Raskutti, G., Wainwright, M. J., and Yu, B. ( 2008 ). Model selection in Gaussian graphical models: High‐dimensional consistency of l1‐regularized MLE. Advances in Neural Information Processing Systems (NIPS) 21, 1329 – 1336. Curran Associates Inc.en_US
dc.identifier.citedreferenceLiu, H., Chen, X., Lafferty, J., and Wasserman, L. ( 2010 ). Graph‐valued regression. Proceedings of Advances in Neural Information Processing Systems (NIPS) 23, 1423 – 1431. Curran Associates Inc.en_US
dc.identifier.citedreferenceManning, C. and Schutze, H. ( 1999 ). Foundations of Statistical Natural Language Processing. Boston, MA: MIT Press.en_US
dc.identifier.citedreferenceMazumder, R. and Hastie, T. ( 2012 ). Exact covariance thresholding into connected components for large‐scale graphical lasso. Journal of Machine Learning Research 13, 781 – 794.en_US
dc.identifier.citedreferenceMeinshausen, N. and Bühlmann, P. ( 2006 ). High dimensional graphs and variable selection with the Lasso. Annals of Statistics 34, 1436 – 1462.en_US
dc.identifier.citedreferenceMeinshausen, N. and Bühlmann, P. ( 2010 ). Stability selection. Journal of the Royal Statistical Soceity 72, 417 – 473.en_US
dc.identifier.citedreferenceMinella, A. C., Grim, J. E., Welcker, M., and Clurman, B. E. ( 2007 ). p53 and scffbw7 cooperatively restrain cyclin e‐associated genome instability. Oncogene 26, 6948 – 6953.en_US
dc.identifier.citedreferenceMitelman, F., Merterns, F., and Johansson, B. ( 1997 ). A breakpoint map of recurrent chromosomal rearrangements in human neoplasia. Nature Genetics 15, 417 – 474.en_US
dc.identifier.citedreferenceNegrini, M., Sabbioni, S., Possati, L., Rattan, S., Corallini, A., Barbanti‐Brodano, G., and Croce, C. M. ( 1994 ). Suppression of tumorigenicity of breast cancer cells by microcell‐mediated chromosome transfer: Studies on chromosomes 6 and 11. Cancer Research 54, 1331 – 1336.en_US
dc.identifier.citedreferencePeng, J., Wang, P., Zhou, N., and Zhu, J. ( 2009 ). Partial correlation estimation by joint sparse regression model. Journal of the American Statistics Association 104, 735 – 746.en_US
dc.identifier.citedreferenceRavikumar, P., Wainwright, M. J., and Lafferty, J. D. ( 2010 ). High‐dimensional ising model selection using ℓ 1 ‐regularized logistic regression. Annals of Statistics 38, 1287 – 1319.en_US
dc.identifier.citedreferenceRocha, G. V., Zhao, P., and Yu, B. ( 2008 ). A path following algorithm for sparse pseudo‐likelihood inverse covariance estimation (splice). Technical Report 759, Department of Statistics, UC Berkeley.en_US
dc.identifier.citedreferenceRothman, A. J., Bickel, P. J., Levina, E., and Zhu, J. ( 2008 ). Sparse permutation invariant covariance estimation. Electronic Journal of Statistics 2, 494 – 515.en_US
dc.identifier.citedreferenceSinha, S., Singh, R. K., Alam, N., Roy, A., Rouchoudhury, S., and Panda, C. K. ( 2008 ). Alterations in candidate genes PHF2, FANCC, PTCH1 and XPA at chromosomal 9q22.3 region: Pathological significance in early‐ and late‐ onset breast carcinoma. Molecular Cancer 7, 84.en_US
dc.identifier.citedreferenceWang, P., Chao, D. L., and Hsu, L. ( 2011 ). Learning oncogenic pathways from binary genomic instability data. Biometrics 67, 164 – 173.en_US
dc.identifier.citedreferenceWitten, D. M., Friedman, J. H., and Simon, N. ( 2011 ). New insights and faster computations for the graphical lasso. Journal of Computational and Graphical Statistics 20, 892 – 900.en_US
dc.identifier.citedreferenceWoods, J. ( 1978 ). Markov image modeling. IEEE Transactions on Automatic Control 23, 846 – 850.en_US
dc.identifier.citedreferenceYang, Z. Q., Streicher, K. L., Ray, M. E., Abrams, J., and Ethier, S. P. ( 2006 ). Multiple interacting oncogenes on the 8p11‐p12 amplicon in human breast cancer. Cancer Research 66, 11632 – 11634.en_US
dc.identifier.citedreferenceYin, J. and Li, H. ( 2011 ). A sparse conditional Gaussian graphical model for analysis of genetical genomics data. Annals of Applied Statistics 5, 2630 – 2650.en_US
dc.identifier.citedreferenceYuan, M. ( 2010 ). Sparse inverse covariance matrix estimation via linear programming. Journal of Machine Learning Research 11, 2261 – 2286.en_US
dc.identifier.citedreferenceYuan, M. and Lin, Y. ( 2007 ). Model selection and estimation in the Gaussian graphical model. Biometrika 94, 19 – 35.en_US
dc.identifier.citedreferenceZhao, H., Langerød, A., Ji, Y., Nowels, K. W., Nessland, J. M., Tibshirani, R., Bukholm, I. K., Kåresen, R., Botstein, D., and Børresen‐Dale, A. L. ( 2004 ). Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Molecular Biology of the Cell 15, 2523 – 2536.en_US
dc.identifier.citedreferenceAdélaïde, J., Chaffanet, M., Imbert, A., Allione, F., Geneix, J., Popovici, C., van Alewijk, D., Trapman, J., Zeillinger, R., Børrensen‐Dale, A. L., Lidereau, R., Birnbaum, D., and Pe'busque, M. J. ( 1998 ). Chromosome region 8p11‐p21: Refined mapping and molecular alterations in breast cancer. Genes, Chromosomes & Cancer 22, 186 – 199.en_US
dc.identifier.citedreferenceAli, I. U., Lidereau, R., Theillet, C., and Callahan, R. ( 1987 ). Reduction to homozygosity of genes on chromosome 11 in human breast neoplasia. Science 238, 185 – 188.en_US
dc.identifier.citedreferenceBarabási, A. L. and Albert, R. ( 1999 ). Emergence of scaling in random networks. Science 286, 509 – 512.en_US
dc.identifier.citedreferenceBergamaschi, A., Kim, Y. H., Wang, P., Sørlie, T., Hernandez‐Boussard, T., Lonning, P. E., Tibshirani, R., Børresen‐Dale, A. L., and Pollack, J. R. ( 2006 ). Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene‐expression subtypes of breast cancer. Genes, Chromosomes & Cancer 45, 1033 – 1040.en_US
dc.identifier.citedreferenceCai, T. T., Li, H., Liu, W., and Xie, J. ( 2013 ). Covariate adjusted precision matrix estimation with an application in genetical genomics. Biometrika 100, 139 – 156.en_US
dc.identifier.citedreferenceCai, T. T., Liu, W., and Luo, X. ( 2011 ). A constrained ℓ 1 minimization approach to sparse precision matrix estimation. Journal of American Statistical Association 106, 594 – 607.en_US
dc.identifier.citedreferenceConte, N., Charafe‐Jauffret, E., Adélaïde, J., Ginestier, C., Geneix, J., Isnardon, D., Jacquemier, J., and Birnbaum, D. ( 2002 ). Carcinogenesis and translational controls: Tacc1 is down‐regulated in human cancers and associates with MRNA regulators. Oncogene 21, 5619 – 5630.en_US
dc.identifier.citedreferenced'Aspremont, A., Banerjee, O., and El Ghaoui, L. ( 2008 ). First‐order methods for sparse covariance selection. SIAM Journal on Matrix Analysis and its Applications 30, 56 – 66.en_US
dc.identifier.citedreferenceDevilee, P., van Vliet, M., van Sloun, P., Kuipers Dijkshoorn, N., Hermans, J., Pearson, P. L., and Cornelisse, C. J. ( 1991 ). Allelotype of human breast carcinoma: A second major site for loss of helcrozygosity is on chromosome 6q. Oncogene 6, 1705 – 1711.en_US
dc.identifier.citedreferenceFriedman, J., Hastie, T., and Tibshirani, R. ( 2008 ). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432 – 441.en_US
dc.identifier.citedreferenceFriedman, J., Hastie, T., and Tibshirani, R. ( 2010 ). Regularized paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1 – 22.en_US
dc.identifier.citedreferenceFu, W. J. ( 1998 ). Penalized regressions: The bridge versus the lasso. Journal of Computational and Graphical Statistics 7, 397 – 416.en_US
dc.identifier.citedreferenceGeisler, S., Børresen‐Dale, A. L., Johnsen, H., Aas, T., Geisler, J., Akslen, L. A., Anker, G., and Lønning, P. E. ( 2003 ). Tp53 gene mutations predict the response to neoadjuvant treatment with 5‐fluorouracil and mitomycin in locally advanced breast cancer. Clinical Cancer Research 9, 5582 – 5588.en_US
dc.identifier.citedreferenceGuo, J., Levina, E., Michailidis, G., and Zhu, J. ( 2010 ). Joint estimation of multiple graphical models. Biometrika 98, 1 – 15.en_US
dc.identifier.citedreferenceGuo, J., Levina, E., Michailidis, G., and Zhu, J. ( 2010 ). Joint structure estimation for categorical Markov networks. Technical Report #507, Department of Statistics, University of Michigan, Ann Arbor.en_US
dc.identifier.citedreferenceGuo, J., Levina, E., Michailidis, G., and Zhu, J. ( 2014 ). Estimating heterogeneous graphical models for discrete data with an application to roll call voting. Annals of Applied Statistics. Accepted.en_US
dc.identifier.citedreferenceHassner, M. and Sklansky, J. ( 1980 ). The use of Markov random fields as models of texture. Computer Graphics Image Processing 12, 357 – 370.en_US
dc.identifier.citedreferenceHöefling, H. and Tibshirani, R. ( 2009 ). Estimation of sparse binary pairwise Markov networks using pseudo‐likelihoods. Journal of Machine Learning Research 10, 883 – 906.en_US
dc.identifier.citedreferenceHuang, T. H., Yeh, P. L., Martin, M. B., Straub, R. E., Gilliam, T. C., Caldwell, C. W., and Skibba, J. L. ( 1995 ). Genetic alterations of microsatellites on chromosome 18 in human breast carcinoma. Diagnostic Molecular Pathology 4, 66 – 72.en_US
dc.identifier.citedreferenceIsing, E. ( 1925 ). Beitrag zur theorie der ferromagnetismus. Zeitschrift für Physik 31, 253 – 258.en_US
dc.identifier.citedreferenceLam, C. and Fan, J. ( 2009 ). Sparsistency and rates of convergence in large covariance matrices estimation. Annals of Statistics 37, 4254 – 4278.en_US
dc.identifier.citedreferenceLassus, H., Salovaara, R., Altonen, L. A., and Butzow, R. ( 2001 ). Allelic analysis of serous ovarian carcinoma reveals two putative tumor suppressor loci at 18q22‐q23 distal to smad4, smad2, and dcc. American Journal of Pathology 159, 35 – 42.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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