Efficient parameterization of large-scale dynamic models based on relative measurements
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
[PDF][PDF] Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich… - …, 2019 - mediatum.ub.tum.de
Motivation: Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich… - Bioinformatics …, 2020 - pubmed.ncbi.nlm.nih.gov
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
[PDF][PDF] Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schalte, F Frohlich, J Hasenauer… - …, 2020 - scholar.archive.org
Motivation: Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
[PDF][PDF] Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schalte, F Frohlich… - …, 2020 - scholar.harvard.edu
Motivation: Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
[HTML][HTML] Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich, J Hasenauer… - …, 2020 - ncbi.nlm.nih.gov
Results Here, we propose a novel hierarchical approach combining (i) the efficient analytic
evaluation of optimal scaling, offset and error model parameters with (ii) the scalable …
evaluation of optimal scaling, offset and error model parameters with (ii) the scalable …
Efficient parameterization of large-scale dynamic models based on relative measurements.
L Schmiester, Y Schälte, F Fröhlich… - …, 2020 - search.ebscohost.com
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich, J Hasenauer… - bioRxiv, 2019 - biorxiv.org
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
[PDF][PDF] Efficient parameterization of large-scale dynamic models based on relative measurements
L Schmiester, Y Schälte, F Fröhlich, J Hasenauer… - …, 2020 - academic.oup.com
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
Efficient parameterization of large-scale dynamic models based on relative measurements.
L Schmiester, Y Schälte, F Fröhlich… - Bioinformatics (Oxford …, 2020 - europepmc.org
Results Here, we propose a novel hierarchical approach combining (i) the efficient analytic
evaluation of optimal scaling, offset and error model parameters with (ii) the scalable …
evaluation of optimal scaling, offset and error model parameters with (ii) the scalable …