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