Cargando…
Modeling aspects of the language of life through transfer-learning protein sequences
BACKGROUND: Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applications retrieving related proteins is becoming too...
Autores principales: | Heinzinger, Michael, Elnaggar, Ahmed, Wang, Yu, Dallago, Christian, Nechaev, Dmitrii, Matthes, Florian, Rost, Burkhard |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918593/ https://www.ncbi.nlm.nih.gov/pubmed/31847804 http://dx.doi.org/10.1186/s12859-019-3220-8 |
Ejemplares similares
-
Embeddings from protein language models predict conservation and variant effects
por: Marquet, Céline, et al.
Publicado: (2021) -
Light attention predicts protein location from the language of life
por: Stärk, Hannes, et al.
Publicado: (2021) -
Embeddings from deep learning transfer GO annotations beyond homology
por: Littmann, Maria, et al.
Publicado: (2021) -
Protein embeddings and deep learning predict binding residues for various ligand classes
por: Littmann, Maria, et al.
Publicado: (2021) -
Clustering FunFams using sequence embeddings improves EC purity
por: Littmann, Maria, et al.
Publicado: (2021)