Cargando…
Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network
The increased diversity and scale of published biological data has to led to a growing appreciation for the applications of machine learning and statistical methodologies to gain new insights. Key to achieving this aim is solving the Relationship Extraction problem which specifies the semantic inter...
Autores principales: | David, Rakesh, Menezes, Rhys-Joshua D., De Klerk, Jan, Castleden, Ian R., Hooper, Cornelia M., Carneiro, Gustavo, Gilliham, Matthew |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7813825/ https://www.ncbi.nlm.nih.gov/pubmed/33462256 http://dx.doi.org/10.1038/s41598-020-80441-8 |
Ejemplares similares
-
Using the SUBcellular database for Arabidopsis proteins to localize the Deg protease family
por: Tanz, Sandra K., et al.
Publicado: (2014) -
SUBA4: the interactive data analysis centre for Arabidopsis subcellular protein locations
por: Hooper, Cornelia M., et al.
Publicado: (2017) -
SUBA3: a database for integrating experimentation and prediction to define the SUBcellular location of proteins in Arabidopsis
por: Tanz, Sandra K., et al.
Publicado: (2013) -
Subcellular mRNA localisation at a glance
por: Parton, Richard M., et al.
Publicado: (2014) -
Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures
por: Husain, Syed Sameed, et al.
Publicado: (2023)