Guillaume Richard — accepted AI-conference papers

2026

Bimodal masked language modeling for bulk RNA-seq and DNA methylation representation learning

Maxence Gélard, Hakim Benkirane, Thomas Pierrot, Guillaume Richard, Paul-Henry Cournède

ICML 2026poster

Oncologists are increasingly relying on multiple modalities to model the complexity of diseases. Within this landscape, transcriptomic and epigenetic data have proven to be particularly instrumental and play an increasingly vital role in clinical applications. However, their integration into multimo…

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2024

Multi-modal Transfer Learning between Biological Foundation Models

Juan Jose Garau-Luis, Patrick Philippe Bordes, Liam Gonzalez, Maša Roller, Bernardo P de Almeida, Christopher F. Blum +5

NeurIPS 2024poster

Biological sequences encode fundamental instructions for the building blocks of life, in the form of DNA, RNA, and proteins. Modeling these sequences is key to understand disease mechanisms and is an active research area in computational biology. Recently, Large Language Models have shown great prom…

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