Philippe Auguste Robert

This study evaluates active learning strategies for antibody-antigen binding prediction in library-on-library settings, finding three algorithms that significantly improve efficiency and reduce experimental costs.

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper uses in silico simulations to show that immunodominance in germinal centers is driven by the most immunogenic antigen, but increased vaccine valence may reduce this dominance.

The paper develops AIRRTM, a generative model that identifies and generates disease-associated adaptive immune receptor sequences using only repertoire-level immune status data.

The paper introduces immuneREF, a method to quantify immune repertoire similarity across health and disease, finding that healthy and diseased repertoires are more similar than previously thought.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

The paper develops an agent-based model to evaluate CTL killing mechanisms using 2-photon imaging data, finding that increased contact frequency with infected cells raises killing probability, independent of signal integration.

This study uses an agent-based model to show that TCR affinity modulation during immunological synapse formation depends on ligand density and F-actin dynamics, with centripetal transport and feedback mechanisms playing key roles.

This paper introduces a 3D structural model for simulating germinal center responses to multi-epitope vaccines, showing that epitope cocktails enhance cross-reactive antibody development.

MoonFit provides a C++ interface for fitting ODE models, enabling biologists to perform simulations and optimizations without programming knowledge while allowing customization by programmers.