Sven Degroeve

Professor

Ghent University

Belgium

Research Interests

Explore related searches

Professors in Belgium Professors in Microbiology Microbiology professors in Belgium

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Sven Degroeve is a Professor at Ghent University in Belgium. His research focuses on the development and application of advanced computational methods in proteomics, with recent articles exploring topics such as machine learning models for proteomics, peptide identification, and ion mobility prediction. Dr. Degroeve's work significantly contributes to the evolving field of data-driven proteomics, enhancing both research methodologies and practical applications in biomedical contexts.

Articles (12)

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (10)

Magnus Palmblad

Associate Professor

Universitair Medisch Centrum Utrecht

NETHERLANDS

View details

Mathias Wilhelm

Prof.

Technical University of Munich

GERMANY

View details

Eduard Sabidó

Head

SPAIN

View details

Pathmanaban Ramasamy

Ghent University

BELGIUM

View details

Lennart Martens

Full Professor of Systems Biology

Universiteit Gent

BELGIUM

View details

Katleen Van Uytfanghe

BELGIUM

View details

Maarten Dhaenens

Ghent University

BELGIUM

View details

Viktoria Dorfer

University of Applied Sciences Upper Austria

AUSTRIA

View details

Veit Schwämmle

University of Southern Denmark

DENMARK

View details

Lukas Käll

KTH Royal Institute of Technology

SWEDEN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Sven Degroeve

Research Interests

Explore related searches

Contact this professor

About

Articles (12)

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0

Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling

Updated MS²PIP web server supports cutting-edge proteomics applications

Toward an Integrated Machine Learning Model of a Proteomics Experiment

psm_utils: A High-Level Python API for Parsing and Handling Peptide-Spectrum Matches and Proteomics Search Results

MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates

Cov-MS: A Community-Based Template Assay for Mass-Spectrometry-Based Protein Detection in SARS-CoV-2 Patients

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics

The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows

Generalized Calibration Across Liquid Chromatography Setups for Generic Prediction of Small-Molecule Retention Times

Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries

Collaborators (10)

Magnus Palmblad

Mathias Wilhelm

Eduard Sabidó

Pathmanaban Ramasamy

Lennart Martens

Katleen Van Uytfanghe

Maarten Dhaenens

Viktoria Dorfer

Veit Schwämmle

Lukas Käll

Make your next move.