| Moment-based inference predicts bimodality in transient gene expression C Zechner, J Ruess, P Krenn, S Pelet, M Peter, J Lygeros, H Koeppl Proceedings of the National Academy of Sciences 109 (21), 8340-8345, 2012 | 256 | 2012 |
| Inferring causal molecular networks: empirical assessment through a community-based effort SM Hill, LM Heiser, T Cokelaer, M Unger, NK Nesser, DE Carlin, Y Zhang, ... Nature Methods 13 (4), 310-318, 2016 | 243 | 2016 |
| Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings C Zechner, M Unger, S Pelet, M Peter, H Koeppl Nature Methods 11 (2), 197-202, 2014 | 156 | 2014 |
| Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge AL Tarca, M Lauria, M Unger, E Bilal, S Boue, K Kumar Dey, J Hoeng, ... Bioinformatics 29 (22), 2892-2899, 2013 | 131 | 2013 |
| Verification of systems biology research in the age of collaborative competition P Meyer, LG Alexopoulos, T Bonk, A Califano, CR Cho, A De La Fuente, ... Nature Biotechnology 29 (9), 811-815, 2011 | 98 | 2011 |
| ‘Glocal’robustness analysis and model discrimination for circadian oscillators M Hafner, H Koeppl, M Hasler, A Wagner PLoS Computational Biology 5 (10), e1000534, 2009 | 97 | 2009 |
| Attention-based transformers for instance segmentation of cells in microstructures T Prangemeier, C Reich, H Koeppl 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2020 | 96 | 2020 |
| Inverse reinforcement learning in swarm systems A Šošić, WR KhudaBukhsh, AM Zoubir, H Koeppl 16th Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 1413–1421, 2017 | 90 | 2017 |
| Effect of network architecture on synchronization and entrainment properties of the circadian oscillations in the suprachiasmatic nucleus M Hafner, H Koeppl, D Gonze PLoS Computational Biology 8 (3), e1002419, 2012 | 81 | 2012 |
| Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning K Cui, H Koeppl International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 76 | 2021 |
| Lumpability abstractions of rule-based systems J Feret, T Henzinger, H Koeppl, T Petrov Theoretical Computer Science 431, 137-164, 2012 | 65 | 2012 |
| Digitally enhanced analog circuits: System aspects B Murmann, C Vogel, H Koeppl 2008 IEEE International Symposium on Circuits and Systems (ISCAS), 560-563, 2008 | 65 | 2008 |
| Spatial simulations in systems biology: from molecules to cells M Klann, H Koeppl International Journal of Molecular Sciences 13 (6), 7798-7827, 2012 | 63 | 2012 |
| A cellular system for spatial signal decoding in chemical gradients B Hegemann, M Unger, SS Lee, I Stoffel-Studer, J van den Heuvel, ... Developmental Cell 35 (4), 458-470, 2015 | 61 | 2015 |
| Spatial modeling of vesicle transport and the cytoskeleton: the challenge of hitting the right road M Klann, H Koeppl, M Reuss PloS one 7 (1), e29645, 2012 | 57 | 2012 |
| Jump-diffusion approximation of stochastic reaction dynamics: error bounds and algorithms A Ganguly, D Altintan, H Koeppl SIAM Multiscale Modeling & Simulation 13 (4), 1390-1419, 2015 | 53 | 2015 |
| Uncoupled analysis of stochastic reaction networks in fluctuating environments C Zechner, H Koeppl PLoS Computational Biology 10 (12), e1003942, 2014 | 51 | 2014 |
| Stochastic fragments: A framework for the exact reduction of the stochastic semantics of rule-based models J Feret, H Koeppl, T Petrov International Journal of Software and Informatics 7 (4), 527-604, 2013 | 48 | 2013 |
| Hybrid spatial Gillespie and particle tracking simulation M Klann, A Ganguly, H Koeppl Bioinformatics 28 (18), i549-i555, 2012 | 45 | 2012 |
| Global injectivity and multiple equilibria in uni-and bi-molecular reaction networks C Pantea, H Koeppl, G Craciun Discrete Contin. Dyn. Syst. Ser. B 17 (6), 2153-2170, 2012 | 45 | 2012 |
Christoph ZechnerMax Planck Institute of Molecular Cell Biology and Genetics在 mpi-cbg.de 的电子邮件经过验证
