Registro:

Referencias:

• Choudhary, C., Mann, M., Decoding signalling networks by mass spectrometry-based proteomics (2010) Nat Rev Mol Cell Biol., 11 (6), pp. 427-439
• Nagaraj, N., Wisniewski, J.R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Deep proteome and transcriptome mapping of a human cancer cell line (2011) Mol Syst Biol., 7, p. 548
• Schulz, K.R., Danna, E.A., Krutzik, P.O., Nolan, G.P., Single-cell phospho-protein analysis by flow cytometry (2012) Curr Protoc Immunol., CHAPTER 8, pp. 8-17120
• Bendall, S.C., Nolan, G.P., Roederer, M., Chattopadhyay, P.K., A deep profiler's guide to cytometry (2012) Trends Immunol., 33 (7), pp. 323-332
• Bodenmiller, B., Zunder, E.R., Finck, R., Chen, T.J., Savig, E.S., Bruggner, R.V., Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators (2012) Nat Biotechnol., 30 (9), pp. 858-867
• Friedenberger, M., Bode, M., Krusche, A., Schubert, W., Fluorescence detection of protein clusters in individual cells and tissue sections by using toponome imaging system: sample preparation and measuring procedures (2007) Nat Protoc., 2 (9), pp. 2285-2294
• Schubert, W., Bonnekoh, B., Pommer, A.J., Philipsen, L., Böckelmann, R., Malykh, Y., Analyzing proteome topology and function by automated multidimensional fluorescence microscopy (2006) Nat Biotechnol., 24 (10), pp. 1270-1278
• Sachs, K., Itani, S., Carlisle, J., Nolan, G.P., Pe'er, D., Lauffenburger, D.A., Learning signaling network structures with sparsely distributed data (2009) J Comput Biol., 16 (2), pp. 201-212
• Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S., Stochastic gene expression in a single cell (2002) Science., 297 (5584), pp. 1183-1186
• Hasenauer, J., Waldherr, S., Doszczak, M., Radde, N., Scheurich, P., Allgöwer, F., Identification of models of heterogeneous cell populations from population snapshot data (2011) BMC Bioinformatics., 12, p. 125
• Zechner, C., Ruess, J., Krenn, P., Pelet, S., Peter, M., Lygeros, J., Moment-based inference predicts bimodality in transient gene expression (2012) Proc Natl Acad Sci U S A., 109 (21), pp. 8340-8345
• Zechner, C., Unger, M., Pelet, S., Peter, M., Koeppl, H., Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings (2014) Nat Methods., 11 (2), pp. 197-202
• Irish, J.M., Hovland, R., Krutzik, P.O., Perez, O.D., Bruserud, Ø., Gjertsen, B.T., Single cell profiling of potentiated phospho-protein networks in cancer cells. (2004) Cell, 118 (2), pp. 217-218
• Marusyk, A., Almendro, V., Polyak, K., Intra-tumour heterogeneity: a looking glass for cancer? (2012) Nat Rev Cancer., 12 (5), pp. 323-324
• Hasenauer, J., Hasenauer, C., Hucho, T., Theis, F.J., ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics (2014) PLoS Comput Biol., 10 (7), p. 1003686
• Krutzik, P.O., Crane, J.M., Clutter, M.R., Nolan, G.P., High-content single-cell drug screening with phosphospecific flow cytometry (2008) Nat Chem Biol., 4 (2), pp. 132-142
• Zamir, E., Geiger, B., Cohen, N., Kam, Z., Katz, B.Z., Resolving and classifying haematopoietic bone-marrow cell populations by multi-dimensional analysis of flow-cytometry data (2005) Br J Haematol., 129 (3), pp. 420-431
• Needham, C.J., Bradford, J.R., Bulpitt, A.J., Westhead, D.R., Inference in Bayesian networks (2006) Nat Biotechnol., 24 (1), pp. 51-53
• Friedman, N., Linial, M., Nachman, I., Pe'er, D., Using Bayesian networks to analyze expression data (2000) J Comput Biol, 7 (3-4), pp. 601-620
• Geiger, D., Heckerman, D., Learning Gaussian networks (1994) Uncertainty in Artificial Intelligence Proceedings of the Tenth Conference, pp. 235-243. , In: de Mántaras RL, Poole D, editors. San Francisco, CA: Morgan Kaufmann
• Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wieczorek, J., Sheriff, M.R., Grecco, H.E., Nonparametric Bayesian Networks (with discussion) (2011) Bayesian Statistics, 9. , In: Bernardo JM, Bayarri MJ, Berger JO, Dawid AP, Heckerman D, Smith AFM, West M, editors. Oxford, UK: Oxford University Press
• Pe'er, D., Bayesian network analysis of signaling networks: a primer (2005) Sci STKE., 2005 (281), p. 4
• Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A., Nolan, G.P., Causal protein-signaling networks derived from multiparameter single-cell data (2005) Science., 308 (5721), pp. 523-529
• Sasagawa, S., Ozaki, Y.-I., Fujita, K., Kuroda, S., Prediction and validation of the distinct dynamics of transient and sustained Erk activation (2005) Nat Cell Biol., 7 (4), pp. 365-373
• Ferguson, T.S., A Bayesian analysis of some nonparametric problems (1973) Ann Stat, 1, pp. 209-230
• Nobile, A., Fearnside, A., Bayesian finite mixtures with an unknown number of components (2007) Stat Comput, 17, pp. 147-162
• Grzegorczyk, M., Husmeier, D., Edwards, K.D., Ghazal, P., Millar, A.J., Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler (2008) Bioinformatics., 24 (18), pp. 2071-2078
• Pearl, J., A model of self-activated memory for evidential reasoning (1985) Proceedings of the 7th Conference of the Cognitive Science Society, pp. 329-334. , Irvine, CA: University of California
• Madigan, D., York, J., Bayesian graphical models for discrete data (1995) Int Stat Rev, 63, pp. 215-232
• Friedman, N., Koller, D., Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks (2003) Mach Learn., 50, pp. 95-125
• Neal, R.M., Markov chain sampling methods for Dirichlet process mixture models (2000) J Comput Graphical Stat, 9, pp. 249-265
• Fritsch, A., Ickstadt, K., Improved criteria for clustering based on the posterior similarity matrix (2009) Bayesian Anal., 4, pp. 367-392
• (2013) R: A Language and Environment for Statistical Computing, , Vienna, Austria: R Foundation for Statistical Computing
• MacQueen, J.B., Some methods for classification and analysis of multivariate observations (1967) Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297. , Oakland, California: University of California Press
• Ward, J.H., Hierarchical grouping to optimize an objective function (1963) J Am Stat Assoc, 58, pp. 236-244
• Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis (1987) J Comput Appl Math., 20, pp. 53-65
• Vaudry, D., Stork, P.J.S., Lazarovici, P., Eiden, L.E., Signaling pathways for PC-12 cell differentiation: making the right connections (2002) Science., 296 (5573), pp. 1648-1649
• Santos, S.D.M., Verveer, P.J., Bastiaens, P.I.H., Growth factor-induced MAPK network topology shapes Erk response determining PC-12 cell fate (2007) Nat Cell Biol., 9 (3), pp. 324-330
• Grzegorczyk, M., An introduction to Gaussian Bayesian networks (2010) Methods Mol Biol, 662, pp. 121-147
• Verma, T., Pearl, J., Equivalence and synthesis of causal models (1991) Uncertainty in Artificial Intelligence, 6, pp. 225-268. , In: Bonissone P, Henrion M, Kanal LN, Lemmer JF, editors. Cambridge, MA: Elsevier Science Publishers

Citas:

---------- APA ----------

---------- CHICAGO ----------

---------- MLA ----------

---------- VANCOUVER ----------