Factors analysis of protein O-glycosylation site prediction. (December 2017)

• Record Type: Journal Article
• Authors: Yang, Xuemei; Han, Henry
• Is Part Of: Computational biology and chemistry. Volume 71(2017)
• Page Start: 258
• Page End: 263
• Publication Date: 2017-12
• Subjects: Protein -- Machine learning -- Factors analysis -- Correspondence analysis; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.09.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: To improve the prediction accuracy of O -glycosylation sites, and analyze the structure of the O -glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning algorithms' performance in glycosylation site prediction besides demonstrates advantages compared to principal component analysis and nonnegative matrix factorization. In addition, we have found that factor analysis based linear discriminant analysis seem to be a desirable method in O -glycosylation site prediction for its advantage in both accuracy and time complexity than other machine learning methods. To the best of our knowledge, it is the first work to employ factor analysis in glycosylation site prediction and will inspire more future work in this topic.