The data explosion driven by advancements in genomic research, such as high-throughput
sequencing techniques, is constantly challenging conventional methods used in genomics …

Advancements in genomic research such as high-throughput sequencing techniques have
driven modern genomic studies into" big data" disciplines. This data explosion is constantly …

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 …

Protein secondary structure prediction began in 1951 when Pauling and Corey predicted
helical and sheet conformations for protein polypeptide backbone even before the first …

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary
structure predictions, which are increasingly demanded due to the rapid discovery of …

Protein secondary structure prediction can provide important information for protein 3D
structure prediction and protein functions. Deep learning offers a new opportunity to …

Nuclear receptors are key transcription factors that regulate crucial gene networks
responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a …

Abstract Structure-based drug design is frequently used to accelerate the development of
small-molecule therapeutics. Although substantial progress has been made in X-ray …

Peptide-protein interactions contribute a significant fraction of the protein-protein
interactome. Accurate modeling of these interactions is challenging due to the vast …

With the rapid increment of protein sequence data, it is indispensable to develop automated
and reliable predictive methods for protein function annotation. One approach for facilitating …