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- W4386529944 abstract "Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity." @default.
- W4386529944 created "2023-09-09" @default.
- W4386529944 creator A5002616738 @default.
- W4386529944 creator A5016002177 @default.
- W4386529944 creator A5016850339 @default.
- W4386529944 creator A5045171426 @default.
- W4386529944 creator A5049392012 @default.
- W4386529944 creator A5050420323 @default.
- W4386529944 creator A5064369206 @default.
- W4386529944 date "2023-09-08" @default.
- W4386529944 modified "2023-09-30" @default.
- W4386529944 title "A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity" @default.
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