615 citations · 957 across the 23 of their papers we have counts for
26 papers
Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
Gilles Wainrib, Barbara Bodinier, Haitem Dakhli +5
Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with prior studies suggesting that…
OwkinZero: Accelerating Biological Discovery with AI
Nathan Bigaud, Vincent Cabeli, Meltem Gürel +4
While large language models (LLMs) are rapidly advancing scientific research, they continue to struggle with core biological reasoning tasks essential for translational and biomedi…
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors
Mikhail Zaslavskiy, Simon Jégou, Eric W. Tramel +1
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential lead…
Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach
Pierre Courtiol, Eric W. Tramel, Marc Sanselme +1
Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological…
Robust Detection of Covariate-Treatment Interactions in Clinical Trials
Baptiste Goujaud, Eric W. Tramel, Pierre Courtiol +2
Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of dat…
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Stanislas Chambon, Mathieu Galtier, Pierrick Arnal +2
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of…