Project Euclid

References

• Alon, U. (2007). “Network motifs: theory and experimental approaches.” Nature Reviews Genetics, 8(6): 450–461.
• Bhadra, A. and Mallick, B. K. (2013). “Joint high-dimensional Bayesian variable and covariance selection with an application to eQTL analysis.” Biometrics, 69(2): 447–457.
  Zentralblatt MATH: 1274.62722
  Digital Object Identifier: doi:10.1111/biom.12021
  
• Cai, T. T., Li, H., Liu, W., and Xie, J. (2012). “Covariate-adjusted precision matrix estimation with an application in genetical genomics.” Biometrika, 100(1): 139–156.
  Zentralblatt MATH: 1284.62648
  Digital Object Identifier: doi:10.1093/biomet/ass058
  
• Cai, X., Bazerque, J. A., and Giannakis, G. B. (2013). “Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.” PLoS Computational Biology, 9(5): e1003068.
• Chen, M., Ren, Z., Zhao, H., and Zhou, H. (2016). “Asymptotically normal and efficient estimation of covariate-adjusted Gaussian graphical model.” Journal of the American Statistical Association, 111(513): 394–406.
• Colussi, D., Brandi, G., Bazzoli, F., and Ricciardiello, L. (2013). “Molecular pathways involved in colorectal cancer: implications for disease behavior and prevention.” International Journal of Molecular Sciences, 14(8): 16365–16385.
• Dhillon, A. S., Hagan, S., Rath, O., and Kolch, W. (2007). “MAP kinase signalling pathways in cancer.” Oncogene, 26(22): 3279–3290.
• Dobra, A., Lenkoski, A., and Rodriguez, A. (2012). “Bayesian inference for general Gaussian graphical models with application to multivariate lattice data.” Journal of the American Statistical Association.
  Zentralblatt MATH: 1234.62018
  Digital Object Identifier: doi:10.1198/jasa.2011.tm10465
  
• Fang, J. Y. and Richardson, B. C. (2005). “The MAPK signalling pathways and colorectal cancer.” The Lancet Oncology, 6(5): 322–327.
• Frydenberg, M. (1990). “The chain graph Markov property.” Scandinavian Journal of Statistics, 333–353.
  Mathematical Reviews (MathSciNet): MR1096723
  Zentralblatt MATH: 0713.60013
  
• Green, P. J. and Thomas, A. (2013). “Sampling decomposable graphs using a Markov chain on junction trees.” Biometrika, 100(1): 91–110.
  Zentralblatt MATH: 1284.62172
  Digital Object Identifier: doi:10.1093/biomet/ass052
  
• Holt, K. H., Kasson, B. G., and Pessin, J. E. (1996). “Insulin stimulation of a MEK-dependent but ERK-independent SOS protein kinase.” Molecular and Cellular Biology, 16(2): 577–583.
• Johnson, V. E. and Rossell, D. (2010). “On the use of non-local prior densities in Bayesian hypothesis tests.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(2): 143–170.
  Mathematical Reviews (MathSciNet): MR2830762
  Digital Object Identifier: doi:10.1111/j.1467-9868.2009.00730.x
  
• Koster, J. T. (1996). “Markov properties of nonrecursive causal models.” The Annals of Statistics, 24(5): 2148–2177.
  Zentralblatt MATH: 0867.62056
  Digital Object Identifier: doi:10.1214/aos/1069362315
  Project Euclid: euclid.aos/1069362315
  
• Kundu, S. and Kang, J. (2016). “Semiparametric Bayes conditional graphical models for imaging genetics applications.” Stat, 5(1): 322–337.
• Mendoza, M. C., Er, E. E., and Blenis, J. (2011). “The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation.” Trends in Biochemical Sciences, 36(6): 320–328.
• Mitra, R., Müller, P., Liang, S., Yue, L., and Ji, Y. (2013). “A Bayesian graphical model for ChIP-seq data on histone modifications.” Journal of the American Statistical Association, 108(501): 69–80.
  Mathematical Reviews (MathSciNet): MR3174603
  Zentralblatt MATH: 1379.62079
  Digital Object Identifier: doi:10.1080/01621459.2012.746058
  
• Müller, P., Parmigiani, G., and Rice, K. (2006). “FDR and Bayesian multiple comparisons rules.”
• Newton, M. A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). “Detecting differential gene expression with a semiparametric hierarchical mixture method.” Biostatistics, 5(2): 155–176.
  Zentralblatt MATH: 1096.62124
  Digital Object Identifier: doi:10.1093/biostatistics/5.2.155
  
• Ni, Y., Ji, Y., and Müller, P. (2017). “Supplementary Material for “Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis”.” Bayesian Analysis.
• Ni, Y., Stingo, F., and Baladandayuthapani, V. (2018). “Bayesian graphical regression.” Journal of the American Statistical Association, just accepted.
  Zentralblatt MATH: 06577576
  Digital Object Identifier: doi:10.1111/biom.12309
  
• Ni, Y., Stingo, F. C., and Baladandayuthapani, V. (2015). “Bayesian nonlinear model selection for gene regulatory networks.” Biometrics, 71(3): 585–595.
  Zentralblatt MATH: 06577576
  Digital Object Identifier: doi:10.1111/biom.12309
  
• Oates, C. J., Smith, J. Q., and Mukherjee, S. (2016). “Estimating causal structure using conditional DAG models.” Journal of Machine Learning Research, 17(54): 1–23.
  Zentralblatt MATH: 1360.62279
  
• Plotnikov, A., Zehorai, E., Procaccia, S., and Seger, R. (2011). “The MAPK cascades: signaling components, nuclear roles and mechanisms of nuclear translocation.” Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 1813(9): 1619–1633.
• Rothman, A. J., Levina, E., and Zhu, J. (2010). “Sparse multivariate regression with covariance estimation.” Journal of Computational and Graphical Statistics, 19(4): 947–962.
• Rudelson, M. (2008). “Invertibility of random matrices: norm of the inverse.” Annals of Mathematics, 575–600.
  Mathematical Reviews (MathSciNet): MR2434885
  Zentralblatt MATH: 1175.15030
  Digital Object Identifier: doi:10.4007/annals.2008.168.575
  
• Rudelson, M. and Vershynin, R. (2008). “The Littlewood–Offord problem and invertibility of random matrices.” Advances in Mathematics, 218(2): 600–633.
  Zentralblatt MATH: 1139.15015
  Digital Object Identifier: doi:10.1016/j.aim.2008.01.010
  
• Shin, S.-Y., Rath, O., Zebisch, A., Choo, S.-M., Kolch, W., and Cho, K.-H. (2010). “Functional roles of multiple feedback loops in ERK and Wnt signaling pathways that regulate epithelial-mesenchymal transition.” Cancer Research, 70(17): 6715.
• Spirtes, P. (1995). “Directed cyclic graphical representations of feedback models.” In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 491–498. Morgan Kaufmann Publishers Inc.
• Stingo, F. C., Chen, Y. A., Vannucci, M., Barrier, M., and Mirkes, P. E. (2010). “A Bayesian graphical modeling approach to microRNA regulatory network inference.” The Annals of Applied Statistics, 4(4): 2024.
  Zentralblatt MATH: 1220.62142
  Digital Object Identifier: doi:10.1214/10-AOAS360
  Project Euclid: euclid.aoas/1294167808
  
• TCGA (2012). “Comprehensive molecular characterization of human colon and rectal cancer.” Nature, 487(7407): 330–337.
• Telesca, D., Müller, P., Kornblau, S. M., Suchard, M. A., and Ji, Y. (2012a). “Modeling protein expression and protein signaling pathways.” Journal of the American Statistical Association, 107(500): 1372–1384.
  Zentralblatt MATH: 1258.62110
  Digital Object Identifier: doi:10.1080/01621459.2012.706121
  
• Telesca, D., Müller, P., Parmigiani, G., and Freedman, R. S. (2012b). “Modeling dependent gene expression.” The Annals of Applied Statistics, 6(2): 542–560.
  Zentralblatt MATH: 1243.62038
  Digital Object Identifier: doi:10.1214/11-AOAS525
  Project Euclid: euclid.aoas/1339419607
  
• Wang, H. and West, M. (2009). “Bayesian analysis of matrix normal graphical models.” Biometrika, 96(4): 821–834.
  Zentralblatt MATH: 1179.62042
  Digital Object Identifier: doi:10.1093/biomet/asp049
  
• Wang, W., Baladandayuthapani, V., Holmes, C. C., and Do, K.-A. (2013). “Integrative network-based Bayesian analysis of diverse genomics data.” BMC Bioinformatics, 14(Suppl 13): S8.
• Yajima, M., Telesca, D., Ji, Y., and Müller, P. (2015). “Detecting differential patterns of interaction in molecular pathways.” Biostatistics, 16(2): 240–251.
• Zenonos, K. and Kyprianou, K. (2013). “RAS signaling pathways, mutations and their role in colorectal cancer.” World Journal of Gastrointestinal Oncology, 5(5): 97–101.
• Zhang, D., Wells, M. T., Turnbull, B. W., Sparrow, D., and Cassano, P. A. (2005). “Hierarchical graphical models: An application to pulmonary function and cholesterol levels in the normative aging study.” Journal of the American Statistical Association, 100(471): 719–727.
  Zentralblatt MATH: 1117.62458
  Digital Object Identifier: doi:10.1198/016214505000000114
  
• Zhang, L. and Kim, S. (2014). “Learning gene networks under SNP perturbations using eQTL datasets.” PLoS Computational Biology, 10(2): e1003420.
• Zhu, Y., Qiu, P., and Ji, Y. (2014). “TCGA-assembler: open-source software for retrieving and processing TCGA data.” Nature Methods, 11(6): 599–600.