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)
Artikel, Bibliographie

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.
AAAI, Association for the Advancement of Artificial Intelligence. Phoenix, USA (12.-17.02.2016)
Konferenzveröffentlichung, Bibliographie

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)
doi: 10.1098/rsif.2016.0533
Artikel, Bibliographie

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
Artikel, Bibliographie

Koeppl, H. ; Zechner, C. (2014)
Uncoupled analysis of stochastic reaction networks in fluctuating environments.
In: PLOS Computational Biology, 10 (12)
Artikel, Bibliographie

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)
Artikel, Bibliographie

Zechner, C. ; Wadehn, F. ; Koeppl, H. (2014)
Sparse learning of Markovian population models in random environments.
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)
Konferenzveröffentlichung, Bibliographie

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)
Artikel, Bibliographie

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)
Artikel, Bibliographie

Zechner, C. ; Deb, S. ; Koeppl, H. (2013)
Marginal dynamics of stochastic biochemical networks in random environments.
2013 European Control Conference (ECC). Zürich (2013)
Konferenzveröffentlichung, Bibliographie

Zechner, C. ; Nandy, P. ; Unger, M. ; Koeppl, H. (2012)
Optimal variational perturbations for the inference of stochastic reaction dynamics.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC).
Konferenzveröffentlichung, Bibliographie

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)
Artikel, Bibliographie

Nandy, P. ; Unger, M. ; Zechner, C. ; Koeppl, H. (2012)
Optimal Perturbations for the Identification of Stochastic Reaction Dynamics.
16th IFAC Symposium on System Identification.
Konferenzveröffentlichung, Bibliographie

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)
Artikel, Bibliographie

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
Artikel, Bibliographie