CV #

Education and work #

• 2025 - Current : Postdoc, University of Antwerp
• 2020 - 2025 : PhD in Bioscience Engineering, Ghent University
  • Dissertation title: Self-supervised Transformers for Biological Data Modalities
• 2018 - 2020 : MSc in Bioinformatics (Bioscience Engineering), Ghent University
• 2015 - 2018 : BSc in Bioscience Engineering: Cell and Gene Biotechnology, Ghent University
• 2009 - 2015 : Science-Mathematics, Leiepoort Campus Sint-Hendrik Deinze

Additional courses:

• 2023: ACDL (Advanced Course on Data Science & Machine Learning), 5-day course, Castiglione della Pescaia, Italy
• 2023: Causal Machine Learning, semester course at Ghent University
• 2022: Research to Market, 2-day course at Ghent University

Academic output #

• Publications:
  • De Waele, Gaetan, Jim Clauwaert, Gerben Menschaert, and Willem Waegeman. “CpG Transformer for imputation of single-cell methylomes.” Bioinformatics 38, no. 3 (2022): 597-603.
  • De Waele, Gaetan, Gerben Menschaert, and Willem Waegeman.“An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks” eLife 13:RP93242 (2024).
  • Mey, Friederike*, Gaetan De Waele*, Wouter Demeester, Chiara Guidi, Dries Duchi, Tomek Diederen, Hanne Kochuyt et al. “Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli.” New Biotechnology (2025).
  • De Waele, Gaetan, Gerben Menschaert, Peter Vandamme, and Willem Waegeman. “Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction.” Computers in Biology and Medicine (2025).
  • Vergauwe, Fauve, Gaetan De Waele, Andrea Sass, Callum Highmore, Niall Hanrahan, Yoshiki Cook, Mads Lichtenberg et al. “Harnessing machine learning to predict antibiotic susceptibility in Pseudomonas aeruginosa biofilms.” npj Biofilms and Microbiomes 11, no. 1 (2025): 205.

• Preprints:
  • De Waele, Gaetan, Gerben Menschaert, and Willem Waegeman. “A systematic assessment of single-cell language model configurations.” bioRxiv (2025): 2025-04.

(*: shared first author)

• Conference contributions:
  • Oral at ISMB22, Madison, USA. “CpG Transformer for imputation of single-cell methylomes”
  • Oral at AAAI24 LLMs4Bio workshop, Vancouver, Canada. “Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction”
  • Poster at ABLS24, Leuven, Belgium. “Transformers for MALDI-TOF MS-based antimicrobial drug recommendation”
  • Oral at ISMB24, Montreal, Canada. “Transformers for MALDI-TOF MS-based antimicrobial drug recommendation”
  • Poster at VIB.AI Symposium 24, Mechelen, Belgium, “Benchmarking single-cell Language Model building blocks”

• Software:
  • cpg-transformer, code supporting my work on methylation (CpG) data imputation.
  • h5torch, a simple utility that allows PyTorch Dataloading from HDF5 files.
  • bio-attention, definitions for transformers adapted to the work in my PhD.
  • maldi-nn, code supporting my work on MALDI-TOF mass spectrometry.
  • bento-sc, code supporting my work on single-cell transcriptomic language models.
  • cut2min-bucket, a small dataloading utility to use flash-attn with variable length inputs.

Teaching #

• 2020 - 2025 : Teaching assistance for “Machine learning for the Life Sciences” course
• 2024 - 2025 : Tutor for Master thesis “End-to-end de novo metabolomics” by Wout Welvaert
• 2024 - 2025 : Tutor for Master thesis “Variational inference for DTI” by Robbe Claeys
• 2023 - 2024 : Tutor for Master thesis “Novel neural networks for bacterial species prediction from MALDI-TOF data” by Jorge Isaac Cueva Villavicencio
• 2022 - 2023 : Tutor for Master thesis “Two-Branch Neural Networks for Predicting Protein-DNA Interaction” by Natan Tourne
• 2022 - 2023 : Tutor for Master thesis “Designing novel protein sequences using advanced generative models” by Scout Van den Bergh
• 2021 - 2022 : Tutor for Master thesis “Detection of 5mC modification in Nanopore sequencing data using deep learning” by Yari Van Laere
• 2021 - 2022 : Teaching assistance for “UGain Machine Learning” course
• 2021 - 2022 : Tutor for MSc Bioinformatics Design Project “Automated benchmarking of optimized general purpose machine learning classifiers for single cell transcriptomics” by Ruben Allaert, Seoyeon Oh, Natan Tourne, and Christopher Van Hessche
• 2020 - 2021 : Tutor for MSc Bioinformatics Design Project “Benchmarking study of ML methods for bacterial species identification from MALDI-TOF MS data” by Kobe De Temmerman, Triana Forment, Ju Hyung Lee, Natalie Thomas, and Elien Martens