Prediction of the O-glycosylation by support vector machines and semi-supervised learning

Authors:
Hirotaka Sakamoto;Yukiko Nakajima;Kazutoshi Sakakibara;Masahiro Ito;Ikuko Nishikawa
Affiliations:
Ritsumeikan University, College of Information Science and Engineering, Shiga, Japan;Ritsumeikan University, College of Information Science and Engineering, Shiga, Japan;Ritsumeikan University, College of Information Science and Engineering, Shiga, Japan;Ritsumeikan University, College of Life Science;Ritsumeikan University, College of Information Science and Engineering, Shiga, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 3
Cited 0

An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Predicting O-glycosylation sites in mammalian proteins by using SVMs

Computational Biology and Chemistry
Prediction of the O-glycosylation sites in protein by layered neural networks and support vector machines

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Glycosylation is one of the main topics in understanding the life systems. More than a half of the protein is glycosylated to acquire the function, structural stability and biological diversity. O-glycosylation is one of the two main types of the mammalian protein glycosylation. Though it is known to serine or threonine specific, any consensus sequence is still unknown, while the binding process and the consensus sequence are clarified for the other type of N-glycosylation. We use support vector machines (SVM) for the prediction of O-glycosylation sites using the experimental data as the input information such as protein primary sequences, structural and biochemical characters around a prediction target aiming to elucidate the glycosylation mechanism and the existence of any motives. The present paper also reports the results obtained by the semi-supervised learning using transductive SVM considering a possibility of unobserved glycosylation sites, and by the marginalized kernel considering hidden variables.