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Leonard Schmiester
Leonard Schmiester
University of Oslo, Faculty of Medicine
Verified email at medisin.uio.no
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Cited by
Year
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
1322018
COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
M Ostaszewski, A Niarakis, A Mazein, I Kuperstein, R Phair, ...
Molecular systems biology 17 (10), e10387, 2021
792021
PEtab—Interoperable specification of parameter estimation problems in systems biology
L Schmiester, Y Schälte, FT Bergmann, T Camba, E Dudkin, J Egert, ...
PLOS Computational Biology 17 (1), e1008646, 2021
632021
Benchmarking of numerical integration methods for ODE models of biological systems
P Städter, Y Schälte, L Schmiester, J Hasenauer, PL Stapor
Scientific reports 11 (1), 2696, 2021
432021
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
402020
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
232017
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
142020
Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells
L Adlung, P Stapor, C Tönsing, L Schmiester, LE Schwarzmüller, ...
Cell reports 36 (6), 2021
132021
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models
Y Schälte, F Fröhlich, PJ Jost, J Vanhoefer, D Pathirana, P Stapor, ...
Bioinformatics 39 (11), btad711, 2023
12*2023
Mini-batch optimization enables training of ODE models on large-scale datasets
P Stapor, L Schmiester, C Wierling, S Merkt, D Pathirana, BMH Lange, ...
Nature Communications 13 (1), 34, 2022
122022
Efficient gradient-based parameter estimation for dynamic models using qualitative data
L Schmiester, D Weindl, J Hasenauer
Bioinformatics 37 (23), 4493-4500, 2021
112021
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data
A Köhn-Luque, EM Myklebust, DS Tadele, M Giliberto, L Schmiester, ...
Cell Reports Methods 3 (3), 2023
42023
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
42019
Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model. Cell Syst. 7, 567-579. e6
F Fröhlich, T Kessler, D Weindl, A Shadrin, L Schmiester, H Hache, ...
32018
Efficient parameter optimization for ordinary differential equation models of biological processes using semi-quantitative and qualitative data
LG Schmiester
Technische Universität München, 2021
2021
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
Supplementary information-Efficient gradient-based parameter estimation for dynamic models using qualitative data
L Schmiester, D Weindl, J Hasenauer
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