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Leonard Schmiester

Showing results (1-10 of 13) with videos related to

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Bioinformatics (Oxford, England)|July 14, 2021
Efficient gradient-based parameter estimation for dynamic models using qualitative dataLeonard Schmiester, Daniel Weindl, Jan Hasenauer
Journal of Mathematical Biology|July 23, 2020
Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approachLeonard Schmiester, Daniel Weindl, Jan Hasenauer
Bioinformatics (Oxford, England)|July 27, 2019
Efficient parameterization of large-scale dynamic models based on relative measurementsLeonard Schmiester, Yannik Schälte, Fabian Fröhlich, et al.
Scientific Reports|January 30, 2021
Benchmarking of numerical integration methods for ODE models of biological systemsPhilipp Städter, Yannik Schälte, Leonard Schmiester, et al.
Nature Communications|January 11, 2022
Mini-batch optimization enables training of ODE models on large-scale datasetsPaul Stapor, Leonard Schmiester, Christoph Wierling, et al.
Cell Reports|August 11, 2021
Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cellsLorenz Adlung, Paul Stapor, Christian Tönsing, et al.
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|June 26, 2024
Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 InhibitionLeonard Schmiester, Fara Brasó-Maristany, Blanca González-Farré, et al.
Cell Systems|December 4, 2018
Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway ModelFabian Fröhlich, Thomas Kessler, Daniel Weindl, et al.
Bioinformatics (Oxford, England)|November 23, 2023
pyPESTO: a modular and scalable tool for parameter estimation for dynamic modelsYannik Schälte, Fabian Fröhlich, Paul J Jost, et al.
Cell Reports Methods|April 14, 2023
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening dataAlvaro Köhn-Luque, Even Moa Myklebust, Dagim Shiferaw Tadele, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Bioinformatics (Oxford, England)|July 14, 2021
Efficient gradient-based parameter estimation for dynamic models using qualitative dataLeonard Schmiester, Daniel Weindl, Jan Hasenauer
Journal of Mathematical Biology|July 23, 2020
Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approachLeonard Schmiester, Daniel Weindl, Jan Hasenauer
Bioinformatics (Oxford, England)|July 27, 2019
Efficient parameterization of large-scale dynamic models based on relative measurementsLeonard Schmiester, Yannik Schälte, Fabian Fröhlich, et al.
Scientific Reports|January 30, 2021
Benchmarking of numerical integration methods for ODE models of biological systemsPhilipp Städter, Yannik Schälte, Leonard Schmiester, et al.
Nature Communications|January 11, 2022
Mini-batch optimization enables training of ODE models on large-scale datasetsPaul Stapor, Leonard Schmiester, Christoph Wierling, et al.
Cell Reports|August 11, 2021
Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cellsLorenz Adlung, Paul Stapor, Christian Tönsing, et al.
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research|June 26, 2024
Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 InhibitionLeonard Schmiester, Fara Brasó-Maristany, Blanca González-Farré, et al.
Cell Systems|December 4, 2018
Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway ModelFabian Fröhlich, Thomas Kessler, Daniel Weindl, et al.
Bioinformatics (Oxford, England)|November 23, 2023
pyPESTO: a modular and scalable tool for parameter estimation for dynamic modelsYannik Schälte, Fabian Fröhlich, Paul J Jost, et al.
Cell Reports Methods|April 14, 2023
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening dataAlvaro Köhn-Luque, Even Moa Myklebust, Dagim Shiferaw Tadele, et al.
Pageof 2