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Tobias Newmiwaka

dblp:295/2444 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 50% Computational science and engineering · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering › dynamical systems
dynamical systems reconstruction
0.512021
SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology · Bioinform. 2021
Bioinformatics and computational biology
systems biology
0.512021
SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology · Bioinform. 2021

Methods — techniques the papers use, named apart from their topics

ordinary differential equation modeling · 0.5
YearPublicationVenuePosition
2021 SEEDS: data driven inference of structural model errors and unknown inputs for dynamic systems biology
abstract
SUMMARY: Dynamic models formulated as ordinary differential equations can provide information about the mechanistic and causal interactions in biological systems to guide targeted interventions and to design further experiments. Inaccurate knowledge about the structure, functional form and parameters of interactions is a major obstacle to mechanistic modeling. A further challenge is the open nature of biological systems which receive unknown inputs from their environment. The R-package SEEDS implements two recently developed algorithms to infer structural model errors and unknown inputs from output measurements. This information can facilitate efficient model recalibration as well as experimental design in the case of misfits between the initial model and data. AVAILABILITY AND IMPLEMENTATION: For the R-package seeds, see the CRAN server https://cran.r-project.org/package=seeds.
Tobias Newmiwaka, Benjamin Engelhardt, Philipp Wendland, Dominik Kahl, Holger Fröhlich, Maik Kschischo
Bioinform.1