Novel H/ACA box snoRNA mining and secondary structure prediction algorithms
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 5589 LNAI, Page: 538-546
2009
- 2Citations
- 3Captures
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Conference Paper Description
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. © 2009 Springer Berlin Heidelberg.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=69049102190&origin=inward; http://dx.doi.org/10.1007/978-3-642-02962-2_68; http://link.springer.com/10.1007/978-3-642-02962-2_68; http://link.springer.com/content/pdf/10.1007/978-3-642-02962-2_68; https://dx.doi.org/10.1007/978-3-642-02962-2_68; https://link.springer.com/chapter/10.1007/978-3-642-02962-2_68
Springer Nature
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