Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning
The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available training datasets are underrepresented, which like...
Päätekijät: | Igiraneza, AB, Zacharopoulou, P, Hinch, R, Wymant, C, Abeler-Dörner, L, Frater, J, Fraser, C |
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Aineistotyyppi: | Journal article |
Kieli: | English |
Julkaistu: |
Public Library of Science
2024
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Samankaltaisia teoksia
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