Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity

Lorenzo Asti, Guido Uguzzoni, Paolo Marcatili, Andrea Pagnani

Open source

DOI: 10.1371/journal.pcbi.1004870
Published: 2016-04-13
Container: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pcbi.1004870,
  title = {Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity},
  author = {Lorenzo Asti and Guido Uguzzoni and Paolo Marcatili and Andrea Pagnani},
  year = {2016},
  journal = {PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1004870},
  url = {https://doi.org/10.1371/journal.pcbi.1004870}
}

RIS

TY  - JOUR
TI  - Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity
AU  - Lorenzo Asti
AU  - Guido Uguzzoni
AU  - Paolo Marcatili
AU  - Andrea Pagnani
PY  - 2016
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1004870
UR  - https://doi.org/10.1371/journal.pcbi.1004870
ER  -

APA

Asti, L., Uguzzoni, G., Marcatili, P., & Pagnani, A. (2016). Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1004870

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crossref · retrieved 2026-09-25T00:11:15.353Z