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RAIN: machine learning-based identification for HIV-1 bNAbs

Abstract Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Mathilde Foglierini, Pauline Nortier, Rachel Schelling, Rahel R. Winiger, Philippe Jacquet, Sijy O’Dell, Davide Demurtas, Maxmillian Mpina, Omar Lweno, Yannick D. Muller, Constantinos Petrovas, Claudia Daubenberger, Matthieu Perreau, Nicole A. Doria-Rose, Raphael Gottardo, Laurent Perez
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2024-06-01
Schriftenreihe:Nature Communications
Online-Zugang:https://doi.org/10.1038/s41467-024-49676-1
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