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...
Үндсэн зохиолчид: | Igiraneza, AB, Zacharopoulou, P, Hinch, R, Wymant, C, Abeler-Dörner, L, Frater, J, Fraser, C |
---|---|
Формат: | Journal article |
Хэл сонгох: | English |
Хэвлэсэн: |
Public Library of Science
2024
|
Ижил төстэй зүйлс
-
Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.
-н: Aime Bienfait Igiraneza, зэрэг
Хэвлэсэн: (2024-11-01) -
The potential of broadly neutralizing antibodies for HIV prevention
-н: Huub C. Gelderblom, зэрэг
Хэвлэсэн: (2024-05-01) -
Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research
-н: Brian D. Williamson, зэрэг
Хэвлэсэн: (2023-09-01) -
Potential of conventional & bispecific broadly neutralizing antibodies for prevention of HIV-1 subtype A, C & D infections.
-н: Kshitij Wagh, зэрэг
Хэвлэсэн: (2018-03-01) -
Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models
-н: Hinch, R, зэрэг
Хэвлэсэн: (2022)