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Janneke Hendriks

dblp:276/1301 · DBLP profile ↗
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2ranked-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 · 2 · 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
2 papers
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metabolomics
metabolic flux analysis
1.322025
Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): bridging untargeted and targeted flux analysis · Bioinform. 2025
MIAMI--a tool for non-targeted detection of metabolic flux changes for mode of action identification · Bioinform. 2020
Bioinformatics and computational biology
metabolomics
1.322025
Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): bridging untargeted and targeted flux analysis · Bioinform. 2025
MIAMI--a tool for non-targeted detection of metabolic flux changes for mode of action identification · Bioinform. 2020
Bioinformatics and computational biology › metabolomics
stable isotope labeling
1.022025
Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): bridging untargeted and targeted flux analysis · Bioinform. 2025
MIAMI--a tool for non-targeted detection of metabolic flux changes for mode of action identification · Bioinform. 2020
Bioinformatics and computational biology › metabolomics
metabolite identification
0.312025
Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): bridging untargeted and targeted flux analysis · Bioinform. 2025

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

peak picking · 0.9mass isotopomer distribution calculation · 0.9feature grouping · 0.9similarity network · 0.4gas chromatography/mass spectrometry · 0.4
YearPublicationVenuePosition
2025 Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting (IMPACT): bridging untargeted and targeted flux analysis
abstract
SUMMARY: The "Integrative Metabolic-Flux Platform for Analysis, Contextualization, and Targeting" (IMPACT) is a comprehensive, fully modular platform designed for both targeted and untargeted metabolomics analysis in stable-isotope labeling experiments. It facilitates the accurate calculation of mass isotopomer distributions (MIDs) and the annotation of unknown metabolites with a contextualization algorithm, addressing the challenges in metabolomics research. IMPACT integrates an entire preprocessing pipeline for LC-MS data, including peak picking, feature grouping, and peak filling, along with advanced features for isotope detection, MID calculation, and its core feature contextualization, enabling metabolite integration into biological pathway networks. The platform supports various file formats and offers user-friendly online access, making it accessible for researchers seeking to elucidate metabolic pathways and networks with precision and reliability. AVAILABILITY AND IMPLEMENTATION: IMPACT is implemented in Python 3.9 and R 4.3.2, with a front-end in Javascript utilizing the Cytoscape.js library for data visualization. It is available as a docker container and can be accessed online at https://impact.bioinfo.nat.tu-bs.de, providing a user-friendly interface for metabolomics data analysis.
Collin Starke, Janneke Hendriks, Judith Wahrheit, Frank Fluitman, Jules Beekwilder, Florence Miramella Schempp, Karsten Hiller
Bioinform.2
2020 MIAMI--a tool for non-targeted detection of metabolic flux changes for mode of action identification
abstract
SUMMARY: Mass isotopolome analysis for mode of action identification (MIAMI) combines the strengths of targeted and non-targeted approaches to detect metabolic flux changes in gas chromatography/mass spectrometry datasets. Based on stable isotope labeling experiments, MIAMI determines a mass isotopomer distribution-based (MID) similarity network and incorporates the data into metabolic reference networks. By identifying MID variations of all labeled compounds between different conditions, targets of metabolic changes can be detected. AVAILABILITY AND IMPLEMENTATION: We implemented the data processing in C++17 with Qt5 back-end using MetaboliteDetector and NTFD libraries. The data visualization is implemented as web application. Executable binaries and visualization are freely available for Linux operating systems, the source code is licensed under General Public License version 3.
Christian-Alexander Dudek, Carsten Reuse, Regine Fuchs, Janneke Hendriks, Veronique Starck, Karsten Hiller
Bioinform.4