Protein secondary structure prediction using neural networks and deep learning: A review
Literature contains over fifty years of accumulated methods proposed by researchers for
predicting the secondary structures of proteins in silico. A large part of this collection is …
predicting the secondary structures of proteins in silico. A large part of this collection is …
JPred4: a protein secondary structure prediction server
Abstract JPred4 (http://www. compbio. dundee. ac. uk/jpred4) is the latest version of the
popular JPred protein secondary structure prediction server which provides predictions by …
popular JPred protein secondary structure prediction server which provides predictions by …
Protein secondary structure prediction: a review of progress and directions
T Smolarczyk, I Roterman-Konieczna… - Current …, 2020 - ingentaconnect.com
Background: Over the last few decades, a search for the theory of protein folding has grown
into a full-fledged research field at the intersection of biology, chemistry and informatics …
into a full-fledged research field at the intersection of biology, chemistry and informatics …
Sixty-five years of the long march in protein secondary structure prediction: the final stretch?
Protein secondary structure prediction began in 1951 when Pauling and Corey predicted
helical and sheet conformations for protein polypeptide backbone even before the first …
helical and sheet conformations for protein polypeptide backbone even before the first …
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction
Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary
structure predictions, which are increasingly demanded due to the rapid discovery of …
structure predictions, which are increasingly demanded due to the rapid discovery of …
[HTML][HTML] A generic method for assignment of reliability scores applied to solvent accessibility predictions
Background Estimation of the reliability of specific real value predictions is nontrivial and the
efficacy of this is often questionable. It is important to know if you can trust a given prediction …
efficacy of this is often questionable. It is important to know if you can trust a given prediction …
MUFOLD‐SS: new deep inception‐inside‐inception networks for protein secondary structure prediction
Protein secondary structure prediction can provide important information for protein 3D
structure prediction and protein functions. Deep learning offers a new opportunity to …
structure prediction and protein functions. Deep learning offers a new opportunity to …
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties …
Motivation: In recent years, development of a single-method fold-recognition server lags
behind consensus and multiple template techniques. However, a good consensus …
behind consensus and multiple template techniques. However, a good consensus …
SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion …
Accurate prediction of protein secondary structure is essential for accurate sequence
alignment, three‐dimensional structure modeling, and function prediction. The accuracy of …
alignment, three‐dimensional structure modeling, and function prediction. The accuracy of …
[PDF][PDF] CFSSP: Chou and Fasman secondary structure prediction server
TA Kumar - Wide Spectrum, 2013 - researchgate.net
ABSTRACT CFSSP (Chou & Fasman Secondary Structure Prediction Server) is an online
protein secondary structure prediction server. This server predicts regions of secondary …
protein secondary structure prediction server. This server predicts regions of secondary …