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...

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Detalles Bibliográficos
Autores principales: Wang, Sheng, Peng, Jian, Ma, Jianzhu, Xu, Jinbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4707437/
https://www.ncbi.nlm.nih.gov/pubmed/26752681
http://dx.doi.org/10.1038/srep18962

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