O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique | Zendy

Cangzhi Jia | Zendy; Yun Zuo | Zendy; Quan Zou | Zendy

Open Access

O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique

Author(s) -

Cangzhi Jia,

Yun Zuo,

Quan Zou

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty039

Subject(s) - undersampling , oversampling , random forest , naive bayes classifier , principal component analysis , computer science , artificial intelligence , support vector machine , sensitivity (control systems) , classifier (uml) , fuzzy logic , pattern recognition (psychology) , machine learning , correlation , data mining , algorithm , mathematics , bandwidth (computing) , computer network , electronic engineering , engineering , geometry

Protein O-GlcNAcylation (O-GlcNAc) is an important post-translational modification of serine (S)/threonine (T) residues that involves multiple molecular and cellular processes. Recent studies have suggested that abnormal O-G1cNAcylation causes many diseases, such as cancer and various neurodegenerative diseases. With the available protein O-G1cNAcylation sites experimentally verified, it is highly desired to develop automated methods to rapidly and effectively identify O-GlcNAcylation sites. Although some computational methods have been proposed, their performance has been unsatisfactory, particularly in terms of prediction sensitivity.

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