Title:Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review
Volume: 27
Issue: 18
Author(s): Yanwen Li, Feng Pu, Jingru Wang, Zhiguo Zhou, Chunhua Zhang, Fei He*, Zhiqiang Ma*Jingbo Zhang*
Affiliation:
- School of Information Science and Technology, Northeast Normal University, Changchun 130117,China
- School of Information Science and Technology, Northeast Normal University, Changchun 130117,China
- School of Information Science and Technology, Northeast Normal University, Changchun 130117,China
Keywords:
Palmitoylation, machine learning methods, benchmark, feature extraction, bioinformatics, post-translational, post-translational
lipid modification.
Abstract: Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves
a series of biological processes. Although a large number of experimental studies have explored the molecular
mechanism behind the palmitoylation process, the computational methods has attracted much attention
for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical
experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism.
Therefore, the research on the application of machine learning methods to predict palmitoylation sites has
become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we
briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased
methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods
in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.
