Cargando…
Interpretable pairwise distillations for generative protein sequence models
Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed...
Autores principales: | Feinauer, Christoph, Meynard-Piganeau, Barthelemy, Lucibello, Carlo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258900/ https://www.ncbi.nlm.nih.gov/pubmed/35737722 http://dx.doi.org/10.1371/journal.pcbi.1010219 |
Ejemplares similares
-
Generating interacting protein sequences using domain-to-domain translation
por: Meynard-Piganeau, Barthelemy, et al.
Publicado: (2023) -
Distilling identifiable and interpretable dynamic models from biological data
por: Massonis, Gemma, et al.
Publicado: (2023) -
Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon
por: Feinauer, Christoph, et al.
Publicado: (2016) -
Improving pairwise comparison of protein sequences with domain co-occurrence
por: Menichelli, Christophe, et al.
Publicado: (2018) -
Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors
por: Staritzbichler, René, et al.
Publicado: (2021)