Gaetan De Waele

This study evaluates single-cell language model configurations by systematically testing pre-training strategies and identifies best practices for improving performance.

This paper applies conformal prediction to molecular retrieval from mass spectra, producing reliable, spectrum-specific candidate sets with controlled reliability.

The paper uses machine learning to predict tobramycin susceptibility in Pseudomonas aeruginosa biofilms using multiple analytical methods, achieving high accuracy with MALDI-TOF MS and Raman spectroscopy.

The paper introduces Maldi Transformer, a pre-trained model for MALDI-TOF MS data that improves prediction tasks through self-supervised learning.

This paper develops a dual-branch neural network to predict antimicrobial resistance profiles from MALDI-TOF MS data, improving treatment efficiency and enabling fine-tuning across clinical labs.

The paper introduces CpG Transformer, a model for imputing single-cell methylomes, achieving state-of-the-art performance on scBS-seq and scRRBS-seq datasets.