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Intelligent Techniques Analysis for Glycosylation Site Prediction

Author(s):

Alhassan Alkuhlani*, Walaa Gad, Mohamed Roushdy and Abdel-Badeeh M. Salem   Pages 1 - 15 ( 15 )

Abstract:


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.

Keywords:

Glycosylation, glycosylation site prediction, artificial intelligence, machine learning, feature extraction, feature selection

Affiliation:

Computer Science Department, Faculty of Computer and Information Technology, Sana’a University, Sana’a, Information Systems Department, Faculty of Computer and Information Science, Ain Shams University, Cairo, Faculty of Computers & Information Technology, Future University in Egypt, New Cairo, Computer Science Department, Faculty of Computer and Information Science, Ain Shams University, Cairo



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