Sven Degroeve
- Associate Professor
- Staff scientist in the Computational Omics and Systems Biology lab
Contact
Campus Ardoyen, Technologiepark-Zwijnaarde 75, 9052 Ghent
E-mail | LinkedIn | UGent Research Explorer | ORCID
Personalia
Biography
- 2019 - present: Professor - Ghent University
- 2017 - present: Staff Scientist - VIB Ghent
- 2009 - 2017: Postdoc - VIB Ghent
- 2004: PhD - VIB Ghent
- 1999: Master Informatica (Computer Science) - Ghent University
Member of
Research tracks
Expertise
With over two decades of research in the domain of Artificial Intelligence, he has applied his expertise to diverse realms, including Natural Language Processing, Genomics, and Proteomics. His research endeavors focus on:
- Creating tools and predictive models for the analysis of proteomics data
- Elucidating the landscape of protein post-translational modifications by employing deep learning models that simulate molecular behavior across various Proteomics biotechnology platforms.
- Studying pre-trained protein language models, specifically tailored for the purpose of post-translational modification modeling.
- Exploration of deep learning models for graph embeddings, with a focus on investigating protein-protein interactions.
Publications
Key publications
- Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics. J. Proteome Res. (2022)
- DeepLC can predict retention times for peptides that carry as-yet unseen modifications. Nat. Methods 18, 1363–1369 (2021).
- The Age of Data‐Driven Proteomics: How Machine Learning Enables Novel Workflows. Proteomics 20, 1900351 (2020).
- Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Res. 47, (2019).
- Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions. Bioinformatics 35, 5243–5248 (2019).