27 citations · 29 across the 4 of their papers we have counts for
5 papers
Unifying non-Markovian Dynamics and Agent Heterogeneity in Scalable Stochastic Networks
Aurelien Pelissier, Miroslav Phan, Didier Le Bail +2
Stochastic processes underpin dynamics across biology, physics, epidemiology, and finance, yet accurately simulating them remains a major challenge. Classical approaches such as th…
AI Developments for T and B Cell Receptor Modeling and Therapeutic Design
Linhui Xie, Aurelien Pelissier, Yanjun Shao +1
Artificial intelligence (AI) is accelerating progress in modeling T and B cell receptors by enabling predictive and generative frameworks grounded in sequence data and immune conte…
AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking
Chunan Liu, Aurelien Pelissier, Yanjun Shao +4
Accurate prediction of antibody-antigen (Ab-Ag) binding affinity is essential for therapeutic design and vaccine development, yet the performance of current models is limited by no…
T cell receptor binding prediction: A machine learning revolution
Anna Weber, Aurélien Pélissier, María Rodríguez Martínez
Recent advancements in immune sequencing and experimental techniques are generating extensive T cell receptor (TCR) repertoire data, enabling the development of models to predict T…
Practical and scalable simulations of non-Markovian stochastic processes and temporal networks with individual node properties
Aurelien Pelissier, Miroslav Phan, Didier Le Bail +2
Discrete stochastic processes are prevalent in natural systems, with applications in physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often can…