By combining convolutional neural networks (CNN) and long short term memory networks
(LSTM) into the learning structure, this paper presents a supervised learning method called …

This paper develops a novel sequence-based method, tetra-peptide-based increment of
diversity with quadratic discriminant analysis (TPIDQD for short), for protein secondary …

The protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure,
which is proliferating in demand for drug design and development. Further, it can be used to …

Background: Protein secondary structure prediction (PSSP) is a fundamental task in
bioinformatics that is helpful for understanding the three-dimensional structure and …

The prediction of protein secondary structure is the method of finding the way in which an
amino acid sequence causes the protein structure to fold and bend into alpha helices, beta …

Abstract We used Differential Evolution (DE) for the problem of protein structure prediction.
We employed the HP model to represent the folding conformations of a protein in a lattice. In …

The sequence-based prediction of the secondary and supersecondary structures enjoys
strong interest and finds applications in numerous areas related to the characterization and …

A protein secondary structure defines the local conformation of the protein's polypeptide
backbone, which provides important information for protein 3D structure prediction and …

Since 1960s, researchers have proposed several prediction methods, for protein secondary
structure prediction (PSSP), whereas the accuracy of them is no more than 80%. In this case …

Background When characterizing the structural topology of proteins, protein secondary
structure (PSS) plays an important role in analyzing and modeling protein structures …