Background:
Glycosylation is one of the most common post-translation modifications (PTMs) in organism
cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s
recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer,
diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive
laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for
glycosylation site prediction.
Objective:
This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial
intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational
intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In
the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this
paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current
challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are
also included.
Method:
The comparison between these different studies is introduced including many criteria such as databases, feature
extraction and selection, machine learning classification methods, evaluation measures and the performance results.
Results and conclusions:
Many challenges and problems are presented. Consequently, more efforts are needed to get
more accurate prediction models for the three basic types of glycosylation sites.
Computational Prediction of Ubiquitination Proteins Using Evolutionary Profiles and Functional Domain Annotation
