RNA secondary structure prediction in soft computing framework: a review

This article provides an overview of the application of certain soft computing tools namely, genetic algorithms (GAs), simulated annealing (SA), and artificial neural networks (ANNs) in certain tasks of RNA secondary structure prediction. Different tasks like prediction of helix, bulge, hairpin curve, internal loop, and multiloop are, first of all, described along with their basic features. The relevance of using soft computing tools to these problems is then mentioned. These are followed by different approaches along with their merits for addressing some of the aforesaid tasks. Finally some limitations of the current research activity are provided