Cargando…
Predicting improved protein conformations with a temporal deep recurrent neural network
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to...
Autores principales: | Pfeiffenberger, Erik, Bates, Paul A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122789/ https://www.ncbi.nlm.nih.gov/pubmed/30180164 http://dx.doi.org/10.1371/journal.pone.0202652 |
Ejemplares similares
-
Refinement of protein‐protein complexes in contact map space with metadynamics simulations
por: Pfeiffenberger, Erik, et al.
Publicado: (2018) -
Dense neural networks for predicting chromatin conformation
por: Farré, Pau, et al.
Publicado: (2018) -
Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network
por: Zhang, Buzhong, et al.
Publicado: (2018) -
Improving Temporal Stability and Accuracy for Endoscopic Video Tissue Classification Using Recurrent Neural Networks
por: Boers, Tim, et al.
Publicado: (2020) -
Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection
por: Marchi, Erik, et al.
Publicado: (2017)