Cargando…
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present Deep...
Autores principales: | Wang, Sheng, Peng, Jian, Ma, Jianzhu, Xu, Jinbo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707437/ https://www.ncbi.nlm.nih.gov/pubmed/26752681 http://dx.doi.org/10.1038/srep18962 |
Ejemplares similares
-
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields
por: Wang, Sheng, et al.
Publicado: (2015) -
A conditional neural fields model for protein threading
por: Ma, Jianzhu, et al.
Publicado: (2012) -
DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields
por: Shao, Mingfu, et al.
Publicado: (2017) -
Protein structure alignment beyond spatial proximity
por: Wang, Sheng, et al.
Publicado: (2013) -
Protein secondary structure prediction with context convolutional neural network
por: Long, Shiyang, et al.
Publicado: (2019)