An integrated system of neural networks, called SPINE, is established and optimized for
predicting structural properties of proteins. SPINE is applied to three‐state secondary …

Owing to the use of evolutionary information and advanced machine learning protocols,
secondary structures of amino acid residues in proteins can be predicted from the primary …

We report a new approach of using statistical context-based scores as encoded features to
train neural networks to achieve secondary structure prediction accuracy improvement. The …

Proteins can move freely in three‐dimensional space. As a result, their structural properties,
such as solvent accessible surface area, backbone dihedral angles, and atomic distances …

Accurate prediction of protein secondary structure is essential for accurate sequence
alignment, three‐dimensional structure modeling, and function prediction. The accuracy of …

The effect of training a neural network secondary structure prediction algorithm with different
types of multiple sequence alignment profiles derived from the same sequences, is shown to …

EVA is a web‐based server that evaluates automatic structure prediction servers
continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary …

Background Structural properties of proteins such as secondary structure and solvent
accessibility contribute to three-dimensional structure prediction, not only in the ab initio …

Secondary structure prediction recently has surpassed the 70% level of average accuracy,
evaluated on the single residue states helix, strand and loop (Q 3). But the ultimate goal is …

This article attempts to increase the prediction accuracy of residue solvent accessibility and
real‐value backbone torsion angles of proteins through improved learning. Most methods …