DocumentCode
2806998
Title
Quantum-Inspired Evolutionary Algorithm for RNA Multiple Sequence-Structure Alignment
Author
Jie, Zhao Ying ; Zhi, Wang Zheng
Author_Institution
Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China
Volume
6
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
534
Lastpage
537
Abstract
As a classical problem of computational molecular biology, the multiple sequence-structure alignment is also important foundational process. RNA is one of biological polymer, and is different from protein and DNA that the secondary structure of RNA is more conservative than its primary sequence. Therefore, RNA multiple sequences alignment requires not only information of sequences, but also information of secondary structures which those sequences will form. Here, a program - QEA-MRNA, which based on quantum-inspired evolutionary algorithm to align RNA sequences, is proposed. The program introduce a full crossover operator and a fitness function which considering the information of RNA primary sequence and secondary structure, and improving on premature controlling and the convergent speed. The effectiveness and performance of QEA-MRNA are demonstrated by testing cases in BRAliBase.
Keywords
biology computing; convergence; evolutionary computation; macromolecules; mathematical operators; molecular biophysics; BRAliBase; RNA multiple sequence-structure alignment; biological polymer; computational molecular biology; convergent speed; fitness function; full crossover operator; premature controlling; quantum-inspired evolutionary algorithm; secondary structure; Biology computing; Computational biology; DNA; Evolution (biology); Evolutionary computation; Polymers; Proteins; RNA; Sequences; Testing; Quantum-inspired Evolutionary Algorithm; fitness function; full crossover operator; multiple sequence-structure alignment; secondary structure;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location
Tianjin
Print_ISBN
978-0-7695-3736-8
Type
conf
DOI
10.1109/ICNC.2009.68
Filename
5362772
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