33 citations · 37 across the 4 of their papers we have counts for
4 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…
Predicting gene essentiality and drug response from perturbation screens in preclinical cancer models with LEAP: Layered Ensemble of Autoencoders and Predictors
Barbara Bodinier, Gaetan Dissez, Lucile Ter-Minassian +5
High-throughput preclinical perturbation screens, where the effects of genetic, chemical, or environmental perturbations are systematically tested on disease models, hold significa…
Automated calibration of consensus weighted distance-based clustering approaches using sharp
Barbara Bodinier, Dragana Vuckovic, Sabrina Rodrigues +3
In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data sugg…
Automated calibration for stability selection in penalised regression and graphical models
Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nost +2
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated…