Publications by authors named "Barthelemy Meynard-Piganeau"
Article Synopsis
- Scientists want to predict how T cell receptors (TCRs) connect with their targets to help create better medicines for fighting diseases.
- Current methods struggle because they don’t have enough good data and can be biased based on how training data is chosen.
- The new model called TULIP uses incomplete data and a special kind of learning to understand these connections better, showing it can perform well even with new information.
Motivation: Being able to artificially design novel proteins of desired function is pivotal in many biological and biomedical applications. Generative statistical modeling has recently emerged as a new paradigm for designing amino acid sequences, including in particular models and embedding methods borrowed from natural language processing (NLP). However, most approaches target single proteins or protein domains, and do not take into account any functional specificity or interaction with the context.
View Article and Find Full Text PDFMany different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data.
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