Philippe Robert

TL;DR: Here, it is presented in detail how to build an agent-based model (hyphasma), accounting for the dynamics of the germinal center, which encompasses the core quantitative traits of affinity maturation, and allowed to make reliable predictions in previous studies.

TL;DR: The Absolut! as mentioned in this paper software suite enables the generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity.

TL;DR: In this paper, les auteurs analyse les obstacles a surmonter et les difficultes a resoudre pour comparer ces deux sources, dabord en ce qui concerne plusieurs sortes d'atteintes aux biens, ensuite differentes categories de violences.

TL;DR: It is observed that Th1-derived effectors induce distinct transcriptional changes in microglia and astrocytes in addition to commonly regulated transcripts, and this knowledge can help to better understand T cell mediated neuropathologies.

TL;DR: In this article, the authors present an open-source collaborative ecosystem for machine learning analysis of adaptive immune receptor repertoires (AIRR) and demonstrate the broad applicability of immuneML by reproducing a large-scale study on immune state prediction, developing, integrating and applying a novel deep learning method for antigen specificity prediction and showcasing streamlined interpretability-focused benchmarking of AIRR ML.