Novel H/ACA Box snoRNA Mining and Secondary Structure Prediction Algorithms

Authors:
Quan Zou;Maozu Guo;Chunyu Wang;Yingpeng Han;Wenbin Li
Affiliations:
Department of Computer Science and Technology, Harbin Insititute of Technology, Email: guoer713108@gmail.com, Harbin, China 150001;Department of Computer Science and Technology, Harbin Insititute of Technology, Email: guoer713108@gmail.com, Harbin, China 150001;Department of Computer Science and Technology, Harbin Insititute of Technology, Email: guoer713108@gmail.com, Harbin, China 150001;Soybean Research Institute (Key Laboratory of Soybean Biology of Chinese Education Ministry), Northeast Agricultural University, Harbin, China 150030;Soybean Research Institute (Key Laboratory of Soybean Biology of Chinese Education Ministry), Northeast Agricultural University, Harbin, China 150030
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

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Abstract

In this paper we propose a novel H/ACA box snoRNA gene mining algorithm, which is based on ensemble learning and a special secondary structure prediction algorithm. Three contributions are made to improve current mining methods, including enriching the negative training set, using the ensemble classifiers for the class imbalance data, and developing a special secondary structure prediction algorithm for extracting features with high quality. The performance of learning method is proved by cross validation and the mining method is proved by the experiments on genome data.