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Onderzoeker
Ludger Goeminne
- Disciplines:Toegepaste wiskunde, Statistische en numerieke methoden
Affiliaties
- Vakgroep Toegepaste Wiskunde, Informatica en Statistiek (Departement)
Lid
Vanaf22 aug 2013 → 22 sep 2019
Projecten
1 - 1 of 1
- Differentiële proteomics op peptide-, proteïne-, en moduleniveauVanaf1 jan 2015 → 31 dec 2018Financiering: IWT persoonsgebonden financ. - strategische onderzoeksbeurzen, IWT persoonsgebonden financ. - specialisatiebeurzen
Publicaties
1 - 8 van 8
- Robust summarization and inference in proteome-wide label-free quantification(2020)
Auteurs: Adriaan Sticker, Ludger Goeminne, Lennart Martens, Lieven Clement
Pagina's: 1209 - 1219 - MSqRob takes the missing hurdle : uniting intensity- and count-based proteomics(2020)
Auteurs: Ludger Goeminne, Adriaan Sticker, Lennart Martens, Lieven Clement
Pagina's: 6278 - 6287 - Statistical methods for differential proteomics at peptide and protein level(2019)
Auteurs: Ludger Goeminne
- Identification of immune-responsive gene 1 (IRG1) as a target of A20(2018)
Auteurs: Emmy Van Quickelberghe, Arne Martens, Ludger Goeminne, Lieven Clement, Geert van Loo
Pagina's: 2182 - 2191 - Experimental design and data-analysis in label-free quantitative LC/MS proteomics : a tutorial with MSqRob(2018)
Auteurs: Ludger Goeminne, Lieven Clement
Pagina's: 23 - 36 - Peptide-level robust ridge regression improves estimation, sensitivity, and specificity in data-dependent quantitative label-free shotgun proteomics(2016)
Auteurs: Ludger Goeminne, Lieven Clement
Pagina's: 657 - 668 - moFF : a robust and automated approach to extract peptide ion intensities(2016)
Auteurs: Andrea Argentini, Ludger Goeminne, Kenneth Verheggen, Niels Hulstaert, An Staes, Lieven Clement, Lennart Martens
Pagina's: 962 - 965 - Summarization vs. peptide-based models in label-free quantitative proteomics : performance, pitfalls, and data analysis guidelines(2015)
Auteurs: Ludger Goeminne, Andrea Argentini, Lennart Martens, Lieven Clement
Pagina's: 2457 - 2465