CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2013 vol.10 Issue No.01 - Jan.-Feb.
Issue No.01 - Jan.-Feb. (2013 vol.10)
Shubhra Sankar Ray , Center for Soft Comput. Res.: A Nat. Facility, Indian Stat. Inst., Kolkata, India
Sankar K. Pal , Center for Soft Comput. Res.: A Nat. Facility, Indian Stat. Inst., Kolkata, India
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.159
Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.
RNA, Proteins, Dynamic programming, Prediction algorithms, Bioinformatics, Fuzzy logic,machine learning, RNA, DNA, protein, combinatorial optimization, dynamic programming, soft computing, genetic algorithms, neural networks, fuzzy logic, metaheuristics
Shubhra Sankar Ray, Sankar K. Pal, "RNA Secondary Structure Prediction Using Soft Computing", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 1, pp. 2-17, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.159