Leonard Schmiester
Leonard Schmiester
PhD Student, Institute of Computational Biology, Helmholtz Zentrum München
Bestätigte E-Mail-Adresse bei helmholtz-muenchen.de
Titel
Zitiert von
Zitiert von
Jahr
Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model
F Fröhlich, T Kessler, D Weindl, A Shadrin, L Schmiester, H Hache, ...
Cell systems 7 (6), 567-579. e6, 2018
47*2018
Direct Image Reconstruction of Lissajous-Type Magnetic Particle Imaging Data Using Chebyshev-Based Matrix Compression
L Schmiester, M Möddel, W Erb, T Knopp
IEEE Transactions on Computational Imaging 3 (4), 671-681, 2017
82017
Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich, J Hasenauer, D Weindl
Bioinformatics 36 (2), 594-602, 2020
52020
PEtab--interoperable specification of parameter estimation problems in systems biology
L Schmiester, Y Schälte, FT Bergmann, T Camba, E Dudkin, J Egert, ...
arXiv preprint arXiv:2004.01154, 2020
32020
Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach
L Schmiester, D Weindl, J Hasenauer
Journal of Mathematical Biology, 1-21, 2020
1*2020
COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms
M Ostaszewski, A Niarakis, A Mazein, I Kuperstein, R Phair, ...
bioRxiv, 2020
12020
Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes define survival threshold in erythroid progenitor cells
L Adlung, P Stapor, C Tönsing, L Schmiester, LE Schwarzmüller, D Wang, ...
bioRxiv, 866871, 2019
12019
Benchmarking of numerical integration methods for ODE models of biological systems
P Städter, Y Schälte, L Schmiester, J Hasenauer, P Stapor
bioRxiv, 2020
2020
Supplementary information to mini-batch optimization enables training of ODE models on large-scale datasets
P Stapor, L Schmiester, C Wierling, BMH Lange, D Weindl, J Hasenauer
2019
Efficient computation of steady states in large-scale ODE models of biochemical reaction networks
GT Lines, Ł Paszkowski, L Schmiester, D Weindl, P Stapor, J Hasenauer
IFAC-PapersOnLine 52 (26), 32-37, 2019
2019
Mini-batch optimization enables training of ODE models on large-scale datasets
P Stapor, L Schmiester, C Wierling, BMH Lange, D Weindl, J Hasenauer
bioRxiv, 859884, 2019
2019
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