Demo Data from VLDB 2026 / reviewers in the wild / expert
Torkel Loman
dblp:417/1881
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 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 · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › systems biology
dynamical system modeling |
0.9 | 1 | 2025 | PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025 |
Bioinformatics and computational biology › systems biology
parameter estimation |
0.9 | 1 | 2025 | PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025 |
Bioinformatics and computational biology
systems biology |
0.9 | 1 | 2025 | PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025 |
Bioinformatics and computational biology › systems biology › parameter estimation
identifiability analysis |
0.3 | 1 | 2025 | PEtab.jl: advancing the efficiency and utility of dynamic modelling · Bioinform. 2025 |
Methods — techniques the papers use, named apart from their topics
symbolic pre-processing · 0.9SBML import · 0.9ODE solver · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PEtab.jl: advancing the efficiency and utility of dynamic modellingabstractSUMMARY: Dynamic models represent a powerful tool for studying complex biological processes, ranging from cell signalling to cell differentiation. Building such models often requires computationally demanding modelling workflows, such as model exploration and parameter estimation. We developed two Julia-based tools: SBMLImporter.jl, an SBML importer, and PEtab.jl, an importer for parameter estimation problems in the PEtab format, designed to streamline modelling processes. These tools leverage Julia's high-performance computing capabilities, including symbolic pre-processing and advanced ODE solvers. PEtab.jl aims to be a Julia-accessible toolbox that supports the entire modelling pipeline from parameter estimation to identifiability analysis. AVAILABILITY AND IMPLEMENTATION: SBMLImporter.jl and PEtab.jl are implemented in the Julia programming language. Both packages are available on GitHub (github.com/sebapersson/SBMLImporter.jl and github.com/sebapersson/PEtab.jl) as officially registered Julia packages, installable via the Julia package manager. Each package is continuously tested and supported on Linux, macOS, and Windows. Sebastian Persson, Fabian Fröhlich, Stephan Grein, Torkel Loman, Damiano Ognissanti, Viktor Hasselgren, Jan Hasenauer, Marija Cvijovic |
Bioinform. | 4 |