Original paper

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

Volume: 6, Issue: 1
Published: Jan 11, 2016
Abstract
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 DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is...
Paper Details
Title
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields
Published Date
Jan 11, 2016
Volume
6
Issue
1
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.