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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Zou, Q., Guo, M., Wang, C., Han, Y., Li, W. (2009). Novel H/ACA Box snoRNA Mining and Secondary Structure Prediction Algorithms. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_68
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DOI: https://doi.org/10.1007/978-3-642-02962-2_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02961-5
Online ISBN: 978-3-642-02962-2
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