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Mathematical Problems in Engineering
Volume 2013, Article ID 796304, 8 pages
Research Article

A Novel Framework Based on ACO and PSO for RNA Secondary Structure Prediction

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
4School of Computer Science and Information Technology, Northeast Normal University, Changchun 130024, China
5College of Physics and Electronic Information, Wenzhou University, Chashan University Town, Wenzhou, Zhejiang, 325035, China

Received 24 June 2013; Accepted 12 August 2013

Academic Editor: William Guo

Copyright © 2013 Gang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framework (PAF) for RNA secondary structure prediction. PAF consists of crucial stem searching (CSS) and global sequence building (GSB). In CSS, a modified ACO (MACO) is used to search the crucial stems, and then a set of stems are generated. In GSB, we used a modified PSO (MPSO) to construct all the stems in one sequence. We evaluated the performance of PAF on ten sequences, which have length from 122 to 1494. We also compared the performance of PAF with the results obtained from six existing well-known methods, SARNA-Predict, RnaPredict, ACRNA, PSOfold, IPSO, and mfold. The comparison results show that PAF could not only predict structures with higher accuracy rate but also find crucial stems.