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Leonie J. Lorenz

dblp:426/0987 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2025
0000-0002-1471-5782ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 2 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
2 papers
Bioinformatics and computational biology · 79% Computational science and engineering · 21%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › systems biology › computational systems biology
systems biology modeling
0.912025
SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences · Bioinform. 2025
Bioinformatics and computational biology
biomedical text mining
0.712023
PEDL+: protein-centered relation extraction from PubMed at your fingertip · Bioinform. 2023
Computational science and engineering
differential equations
0.312025
SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences · Bioinform. 2025
Computational science and engineering › scientific data management
database curation
0.212023
PEDL+: protein-centered relation extraction from PubMed at your fingertip · Bioinform. 2023
Bioinformatics and computational biology › systems bioinformatics › pathway analysis
pathway reconstruction
0.212023
PEDL+: protein-centered relation extraction from PubMed at your fingertip · Bioinform. 2023

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

browser-based simulation · 0.9SBML translation · 0.9ranking · 0.7natural language processing · 0.7filtering · 0.7
YearPublicationVenuePosition
2025 SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences
abstract
SUMMARY: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.
Leonie J. Lorenz, Antoine Andréoletti, Tung V. N. Nguyen, Henning Hermjakob, Richard G. FitzJohn, Rahuman S. Malik-Sheriff, John A. Lees
Bioinform.1
2023 PEDL+: protein-centered relation extraction from PubMed at your fingertip
abstract
SUMMARY: Relation extraction (RE) from large text collections is an important tool for database curation, pathway reconstruction, or functional omics data analysis. In practice, RE often is part of a complex data analysis pipeline requiring specific adaptations like restricting the types of relations or the set of proteins to be considered. However, current systems are either non-programmable web sites or research code with fixed functionality. We present PEDL+, a user-friendly tool for extracting protein-protein and protein-chemical associations from PubMed articles. PEDL+ combines state-of-the-art NLP technology with adaptable ranking and filtering options and can easily be integrated into analysis pipelines. We evaluated PEDL+ in two pathway curation projects and found that 59% to 80% of its extractions were helpful. AVAILABILITY AND IMPLEMENTATION: PEDL+ is freely available at https://github.com/leonweber/pedl.
Leon Weber-Genzel, Fabio Barth, Leonie J. Lorenz, Fabian Konrath, Kirsten Huska, Jana Wolf, Ulf Leser
Bioinform.3