Cargando…
Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation
Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep lear...
Autores principales: | Rai, Brajesh K., Apgar, James R., Bennett, Eric M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941094/ https://www.ncbi.nlm.nih.gov/pubmed/36806303 http://dx.doi.org/10.1038/s41598-023-28841-4 |
Ejemplares similares
-
Learning meaningful representations of protein sequences
por: Detlefsen, Nicki Skafte, et al.
Publicado: (2022) -
A Meaningful Journey to Predict Fractures with Deep Learning
por: Ha, Jeonghoon
Publicado: (2022) -
Modeling and mitigation of high-concentration antibody viscosity through structure-based computer-aided protein design
por: Apgar, James R., et al.
Publicado: (2020) -
Antibody structure prediction using interpretable deep learning
por: Ruffolo, Jeffrey A., et al.
Publicado: (2021) -
Exploring perceptions of meaningfulness in visual representations of bivariate relationships
por: Beribisky, Nataly, et al.
Publicado: (2019)