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  Generative models of T-cell receptor sequences

Isacchini, G., Sethna, Z., Elhanati, Y., Nourmohammad, A., Walczak, A. M., & Mora, T. (2020). Generative models of T-cell receptor sequences. Physical Review E, 101(6): 062414. doi:10.1103/PhysRevE.101.062414.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-A454-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-A455-1
Genre: Journal Article

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 Creators:
Isacchini, Giulio1, Author              
Sethna, Zachary1, Author
Elhanati, Yuval1, Author
Nourmohammad, Armita1, Author              
Walczak, Aleksandra M.1, Author
Mora, Thierry1, Author
Affiliations:
1Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2516692              

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 Abstract: T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free variational autoencoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable, at a lower computational cost.

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 Dates: 2020-06-152020
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
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Title: Physical Review E
Source Genre: Journal
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Pages: 5 Volume / Issue: 101 (6) Sequence Number: 062414 Start / End Page: - Identifier: ISSN: 2470-0045
ISSN: 2470-0053