We review the current applications of artificial intelligence (AI) in functional genomics. The
recent explosion of AI follows the remarkable achievements made possible by “deep …

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 …

Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation
design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task …

Natural enzymes are delicate biomolecules possessing only marginal thermodynamic
stability. Poorly stable, misfolded, and aggregated proteins lead to huge economic losses in …

ObjectiveData generation in biology and biotechnology has greatly increased in recent
years due to the very rapid development of high-performance technologies. These data are …

In the last decades, huge efforts have been made in the bioinformatics community to
develop machine learning-based methods for the prediction of structural features of proteins …

The recent emergence of deep learning to characterize complex patterns of protein big data
reveals its potential to address the classic challenges in the field of protein data mining …

This paper aims to provide a comprehensive review of the trends and challenges of deep
neural networks for protein secondary structure prediction (PSSP). In recent years, deep …

Motivation Protein structure determination has primarily been performed using X-ray
crystallography. To overcome the expensive cost, high attrition rate and series of trial-and …

The recent studies indicate that the protein secondary structure provides very important
advantages in determining the function of a protein, treating numerous diseases and drug …