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Number of items: 15.

Ruess, J. ; Koeppl, H. ; Zechner, C. (2017):
Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability.
In: The Journal of Chemical Physics, 146 (124122), AIP, [Article]

Studer, L. ; Paulevé, L. ; Zechner, C. ; Reumann, M. ; Rodriguez Martinez, M. ; Koeppl, H. (2016):
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.
Phoenix, USA, AAAI, Association for the Advancement of Artificial Intelligence, Phoenix, USA, 12.-17.02.2016, [Conference or Workshop Item]

Huang, L. ; Hansen, A. S. ; Pauleve, L. ; Unger, M. ; Zechner, C. ; Koeppl, H. (2016):
Reconstructing dynamic molecular states from single-cell time series.
In: Journal of The Royal Society Interface, 13 (122), Royal Society Publishing, ISSN 1742-5689,

Bronstein, L. ; Zechner, C. ; Koeppl, H. (2015):
Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.
In: ScienceDirect - Methods, 85, pp. 22-35. Elsevier, [Article]

Koeppl, H. ; Zechner, C. (2014):
Uncoupled analysis of stochastic reaction networks in fluctuating environments.
In: PLOS Computational Biology, 10 (12), Cornell University, ISSN 1476-928X,

Zechner, C. ; Unger, M. ; Pelet, S. ; Peter, M. ; Koeppl, H. (2014):
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.
In: Nature methods, 11 (2), pp. 197-202. [Article]

Zechner, C. ; Wadehn, F. ; Koeppl, H. (2014):
Sparse learning of Markovian population models in random environments.
pp. 1723-1728, Cornell, IFAC 2014, The 19th World Congress of the International Federation of Automatic Control, Promoting automatic control for the benefit of humankind, Cape Town, South Africa, 24-29 August 2014, [Conference or Workshop Item]

Tarca, A. L. ; Lauria, M. ; Unger, M. ; Bilal, E. ; Boue, S. ; Kumar Dey, K. ; Hoeng, J. ; Koeppl, H. ; Martin, F. ; Meyer, P. ; Nandy, P. ; Norel, R. ; Peitsch, M. ; Rice, J. ; Romero, R. ; Stolovitzky, G. ; Talikka, M. ; Xiang, Y. ; Zechner, C. (2013):
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.
In: Bioinformatics (Oxford, England), 29 (22), pp. 2892-2899. [Article]

Nandy, P. ; Unger, M. ; Zechner, C. ; Dey, K. ; Koeppl, H. (2013):
Learning diagnostic signatures from microarray data using Ll-regularized logistic regression.
In: Systems Biomedicine, 1 (4), Taylor & Francis, [Article]

Zechner, C. ; Deb, S. ; Koeppl, H. (2013):
Marginal dynamics of stochastic biochemical networks in random environments.
pp. 4269-4274, IEEE, 2013 European Control Conference (ECC), Zürich, 2013, [Conference or Workshop Item]

Zechner, C. ; Nandy, P. ; Unger, M. ; Koeppl, H. (2012):
Optimal variational perturbations for the inference of stochastic reaction dynamics.
pp. 5336-5341, IEEE, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), [Conference or Workshop Item]

Koeppl, H. ; Zechner, C. ; Ganguly, A. ; Pelet, S. ; Peter, M. (2012):
Accounting for extrinsic variability in the estimation of stochastic rate constants.
In: International Journal of Robust and Nonlinear Control, 22 (10), pp. 1103-1119. Wiley-Blackwell, [Article]

Nandy, P. ; Unger, M. ; Zechner, C. ; Koeppl, H. (2012):
Optimal Perturbations for the Identification of Stochastic Reaction Dynamics.
pp. 686-691, Elsevier, 16th IFAC Symposium on System Identification, [Conference or Workshop Item]

Zechner, C. ; Ruess, J. ; Krenn, P. ; Pelet, S. ; Peter, M. ; Lygeros, J. ; Koeppl, H. (2012):
Moment-based inference predicts bimodality in transient gene expression.
In: Proceedings of the National Academy of Sciences of the United States of America, 109 (21), pp. 8340-8345. [Article]

Zechner, C. ; Pelet, S. ; Peter, M. ; Koeppl, H. (2011):
Recursive Bayesian estimation of stochastic rate constants from heterogeneous cell populations.
In: IEEE Conference on Decision and Control and European Control Conference, pp. 5837-5843. IEEE, [Article]

This list was generated on Sat Sep 24 00:25:13 2022 CEST.