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Thomas Gosselin-Monplaisir

dblp:426/1037 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0009-3504-201XORCID · reported

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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metabolomics
computational metabolomics
0.912025
MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra · Bioinform. 2025
Bioinformatics and computational biology
isotope labeling
0.912025
MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra · Bioinform. 2025
Bioinformatics and computational biology › structural biology
NMR spectroscopy
0.912025
MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra · Bioinform. 2025

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

spectral parameter extraction · 0.9signal model fitting · 0.9
YearPublicationVenuePosition
2025 MultiNMRFit: a software to fit 1D and pseudo-2D NMR spectra
abstract
MOTIVATION: Nuclear Magnetic Resonance (NMR) is widely used for quantitative analysis of metabolic systems. Accurate extraction of NMR signal parameters-such as chemical shift, intensity, coupling constants, and linewidth-is essential for obtaining information on the structure, concentration, and isotopic composition of metabolites. RESULTS: We present MultiNMRFit, an open-source software designed for high-throughput analysis of 1D NMR spectra, whether acquired individually or as pseudo-2D experiments. MultiNMRFit extracts signal parameters (e.g. intensity, area, chemical shift, and coupling constants) by fitting the experimental spectra using built-in or user-defined signal models that account for multiplicity, providing high flexibility along with robust and reproducible results. The software is accessible both as a Python library and via a graphical user interface, enabling intuitive use by end-users without computational expertise. We demonstrate the robustness and flexibility of MultiNMRFit on 1H, 13C, and 31P NMR datasets collected in metabolomics and isotope labeling studies. AVAILABILITY AND IMPLEMENTATION: MultiNMRFit is implemented in Python 3 and was tested on Unix, Windows, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/NMRTeamTBI/MultiNMRFit/ and https://multinmrfit.readthedocs.io, respectively.
Pierre Millard, Loïc Le Grégam, Svetlana Dubiley, Valeria Gabrielli, Thomas Gosselin-Monplaisir, Guy Lippens, Cyril Charlier
Bioinform.5