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 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
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 Nlinked,
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.
Methods: 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.