Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms
Joint Authors
Huang, Yin-Fu
Wang, Chia-Ming
Liou, Sing-Wu
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-08
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences.
By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns.
Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.
American Psychological Association (APA)
Huang, Yin-Fu& Wang, Chia-Ming& Liou, Sing-Wu. 2013. Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1032699
Modern Language Association (MLA)
Huang, Yin-Fu…[et al.]. Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms. The Scientific World Journal No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-1032699
American Medical Association (AMA)
Huang, Yin-Fu& Wang, Chia-Ming& Liou, Sing-Wu. Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-1032699
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1032699
