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Novel H/ACA box snoRNA mining and secondary structure prediction algorithms

  • Quan Zou*
  • , Maozu Guo
  • , Chunyu Wang
  • , Yingpeng Han
  • , Wenbin Li
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Northeast Agricultural University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings
Pages538-546
Number of pages9
DOIs
StatePublished - 2009
Event4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009 - Gold Coast, QLD, Australia
Duration: 14 Jul 200916 Jul 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5589 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009
Country/TerritoryAustralia
CityGold Coast, QLD
Period14/07/0916/07/09

Keywords

  • Bioinformatics
  • Ensemble learning
  • Gene mining
  • H/ACA box snoRNA
  • MFE (Minimum Free Energy)

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