Qin-Lai Huang, Lida Wang, Shu-Guang Han* and Hua Tang* Pages 527 - 535 ( 9 )
Background: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune recognition and other pathogenesis.Objective: We aim to design a computational method to identify 2'-O-methylation. Methods: Different from the experimental method, we propose a computational workflow to identify the methylation site based on the multi-feature extracting algorithm. Results: With a voting procedure based on 7 best feature-classifier combinations, we achieved Accuracy of 76.5% in 10-fold cross-validation. Furthermore, we optimized features and input the optimized features into SVM. As a result, the AUC reached to 0.813. Conclusion: The RNA sample, especially the negative samples, used in this study are more objective and strict, so we obtained more representative results than state-of-arts studies.
2`-O-methylation, feature extraction, classification algorithm, vote strategy, cross-validation, feature selection.
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Scientific Research Department, Heilongjiang Agricutural Recalmation General Hospital, Heilongjiang, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Department of Pathophysiology, Southwest Medical University, Luzhou 646000