Author

Cedric Bogaert

ORCID 0000-0003-0853-8369Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:proteomics· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:machine learning (classical)· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Bruno FantiD· 2 shared Lena PfitzeriD· 1 shared Lien LybaertiD· 1 shared Nil Adell MilliD· 1 shared Wim Van CriekingeiD· 1 shared

Activity by Year

2022-2022 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 6 mutual co-author links

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2 shared papersBioinformatics

Bruno FantORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 3, 2022

Improving T-cell mediated immunogenic epitope identification via machine learning: the neoIM model

Lena Pfitzer, Lien Lybaert, Cedric Bogaert, Bruno Fant

The paper introduces neoIM, a machine learning model that improves the prediction of immunogenic peptides by classifying them as immunogenic or non-immunogenic with high accuracy.

3 citations

humanproteomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMay 13, 2022

neoMS: Attention-based Prediction of MHC-I Epitope Presentation

Nil Adell Mill, Cedric Bogaert, Wim Van Criekinge, Bruno Fant

neoMS predicts MHC-I epitope presentation using mass spectrometry data, achieving higher accuracy than existing methods and improving immunotherapy response prediction.

3 citations

humanproteomics

𝕏 PostBluesky