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Number of items: 15.

Ruess, J. and Koeppl, H. and Zechner, C. (2017):
Sensitivity estimation for stochastic models of biochemical reaction networks in the presence of extrinsic variability.
In: The Journal of Chemical Physics, AIP, 146, (124122), [Online-Edition: http://aip.scitation.org/doi/10.1063/1.4978940],
[Article]

Studer, L. and Paulevé, L. and Zechner, C. and Reumann, M. and Rodriguez Martinez, M. and Koeppl, H. (2016):
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.
Phoenix, USA, In: AAAI, Association for the Advancement of Artificial Intelligence, Phoenix, USA, 12.-17.02.2016, [Online-Edition: http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/123...],
[Conference or Workshop Item]

Huang, L. and Hansen, A. S. and Pauleve, L. and Unger, M. and Zechner, C. and Koeppl, H. (2016):
Reconstructing dynamic molecular states from single-cell time series.
In: Journal of The Royal Society Interface, Royal Society Publishing, ISSN 1742-5689,
[Online-Edition: http://rsif.royalsocietypublishing.org/content/13/122/201605...],
[Article]

Bronstein, L. and Zechner, C. and Koeppl, H. (2015):
Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.
In: ScienceDirect - Methods, Elsevier, [Online-Edition: http://www.sciencedirect.com/science/journal/aip/10462023],
[Article]

Koeppl, H. and Zechner, C. (2014):
Uncoupled analysis of stochastic reaction networks in fluctuating environments.
In: PLOS Computational Biology, Cornell University, 10, (12), ISSN 1476-928X,
[Online-Edition: http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...],
[Article]

Zechner, C. and Unger, M. and Pelet, S. and Peter, M. and Koeppl, H. (2014):
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings.
In: Nature methods, pp. 197-202, 11, (2), [Online-Edition: http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2794.h...],
[Article]

Zechner, C. and Wadehn, F. and Koeppl, H. (2014):
Sparse learning of Markovian population models in random environments.
Cornell, In: 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, [Online-Edition: http://arxiv.org/abs/1401.4026],
[Conference or Workshop Item]

Tarca, A. L. and Lauria, M. and Unger, M. and Bilal, E. and Boue, S. and Kumar Dey, K. and Hoeng, J. and Koeppl, H. and Martin, F. and Meyer, P. and Nandy, P. and Norel, R. and Peitsch, M. and Rice, J. and Romero, R. and Stolovitzky, G. and Talikka, M. and Xiang, Y. and Zechner, C. (2013):
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.
In: Bioinformatics (Oxford, England), pp. 2892-2899, 29, (22), [Online-Edition: http://www.ncbi.nlm.nih.gov/pubmed/23966112],
[Article]

Nandy, P. and Unger, M. and Zechner, C. and Dey, K. and Koeppl, H. (2013):
Learning diagnostic signatures from microarray data using Ll-regularized logistic regression.
In: Systems Biomedicine, Taylor & Francis, 1, (4), [Online-Edition: http://www.tandfonline.com/doi/full/10.4161/sysb.25271?mobil...],
[Article]

Zechner, C. and Deb, S. and Koeppl, H. (2013):
Marginal dynamics of stochastic biochemical networks in random environments.
IEEE, In: 2013 European Control Conference (ECC), Zürich, 2013, [Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669606],
[Conference or Workshop Item]

Zechner, C. and Nandy, P. and Unger, M. and Koeppl, H. (2012):
Optimal variational perturbations for the inference of stochastic reaction dynamics.
IEEE, In: 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Conference or Workshop Item]

Koeppl, H. and Zechner, C. and Ganguly, A. and Pelet, S. and Peter, M. (2012):
Accounting for extrinsic variability in the estimation of stochastic rate constants.
In: International Journal of Robust and Nonlinear Control, Wiley-Blackwell, pp. 1103-1119, 22, (10), [Online-Edition: http://doi.wiley.com/10.1002/rnc.2804],
[Article]

Nandy, P. and Unger, M. and Zechner, C. and Koeppl, H. (2012):
Optimal Perturbations for the Identification of Stochastic Reaction Dynamics.
Elsevier, In: 16th IFAC Symposium on System Identification, [Online-Edition: http://www.ifac-papersonline.net/Detailed/54661.html],
[Conference or Workshop Item]

Zechner, C. and Ruess, J. and Krenn, P. and Pelet, S. and Peter, M. and Lygeros, J. and 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, pp. 8340-8345, 109, (21), [Online-Edition: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3361437/?tool=pm...],
[Article]

Zechner, C. and Pelet, S. and Peter, M. and 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, IEEE, pp. 5837-5843, [Online-Edition: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumbe...],
[Article]

This list was generated on Sat Oct 19 01:27:53 2019 CEST.