- Work address
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Technologiepark-Zwijnaarde 75, FSVMII
9052 Zwijnaarde - Lennart.Martens@UGent.be
- ORCID iD
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0000-0003-4277-658X
- Bio (via ORCID)
- Dr. Lennart Martens is Professor of Systems Biology at Ghent University, and Group Leader of the Computational Omics and Systems Biology (CompOmics) group at VIB, both in Ghent, Belgium. He has been working in proteomics bioinformatics since his Master’s degree, which focused on the computational interpretation of peptide mass spectra. He then worked as a software developer and framework architect for a software company for a few years, before returning to Ghent University to pursue a Ph.D. focused on proteomics and proteomics informatics. During this time, he worked on the development of high-throughput peptide centric proteomics techniques and on bioinformatics tools to support these new approaches. In 2003 he started the PRIDE proteomics database at the EBI as a Marie Curie fellow of the European Commission. After obtaining his Ph.D. in Sciences: Biotechnology from Ghent University, he rejoined the PRIDE group at EBI, which he coordinated for several years before moving back to Ghent University to take up his current position. Prof. Martens served as the chair of the ABRF iPRG in 2011, sits on the Boards of DELSA (http://www.delsaglobal.org), the Royal Flemish Society for Chemistry – Proteomics, and the Belgian Proteomics Association. He serves as Academic Editor for PLoS ONE, IEEE Journal of Biomedical and Health Informatics and Open Proteomics, and holds Editorial Board positions at PROTEOMICS, BBA Proteins and Proteomics and Molecular BioSystems. An author on more than 130 peer-reviewed papers in the field, he has also co-written two popular Wiley textbooks: Computational Methods for Mass Spectrometry Proteomics (ISBN: 978-0-470-51297-5), and Computational and Statistical Methods for Protein Quantification by Mass Spectrometry (ISBN: 978-1-119-96400-1).
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- Journal Article
- A1
- open access
Toward an integrated machine learning model of a proteomics experiment
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- Journal Article
- A1
- open access
Machine learning on large-scale proteomics data identifies tissue and cell-type specific proteins
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psm_utils : a high-level python API for parsing and handling peptide-spectrum matches and proteomics search results
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Cov²MS : an automated and quantitative matrix-independent assay for mass spectrometric measurement of SARS-CoV-2 nucleocapsid protein
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Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins
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- Miscellaneous
- open access
FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data
(2022) -
- Journal Article
- A1
- open access
An interactive mass spectrometry atlas of histone posttranslational modifications in T-cell acute leukemia
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- Journal Article
- A1
- open access
Immunopeptidomics-based design of mRNA vaccine formulations against Listeria monocytogenes
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- Journal Article
- A1
- open access
Sensitive and specific spectral library searching with CompOmics Spectral library Searching tool and Percolator
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- Journal Article
- A1
- open access
A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics