The Application And Implementation Of Ant Colony Algorithm In RNA Secondary Structure Prediction

RNA is the intermediate carrier of the genetic information and participates inprotein synthesis as a direct template. It plays an important role in cell apoptosis anddifferentiation, biological development and disease incidence, etc.Among the three structures, the stem-loop structure of RNA secondary structure canbe used to analyze RNA functions but also form the foundation for RNA tertiarystructure prediction. Thus, RNA secondary structure prediction hosts greatsignificance. RNA secondary structure prediction includes physicochemical methodand bioinformatics-based method. In the physicochemical method, the molecularstructure of RNA can be identified by X-ray crystallography and nuclear magneticresonance. To obtain high measurement precision, the method demands high onsoftware and hardware. Besides, due to the rapid degradation of RNA molecules, it isdifficult to crystallize and predict. Therefore, this method is suitable for RNAsecondary structure prediction with the number of basic groups smaller than100. Theprediction of RNA secondary structure with bioinformatics can enhance theacknowledging of spatial structure of RNA molecule and its biological functions. Sofar, the minimum free energy algorithm is a relatively common method of prediction,which transforms the structure prediction into combinational optimization. Thisresearch, on the basis of the superiority of ant colony algorithm in combinationaloptimization, believed that ant colony algorithm could be used in RNA secondarystructure prediction. To this end, a reasonable algorithm framework was designed,obtaining certain effects.This research firstly introduced RNA secondary structure， the mathematicalmodel and representation, which laid a theoretical foundation for the predictionalgorithm. Then, this research elaborated the biological components of RNA,classifications and functions. The current algorithms of RNA secondary structureprediction were also introduced and analyzed. Next, this research introduced the principle and application of ant colonyalgorithm, designed and solved the ant colony algorithm for RNA secondary structureprediction. Through experiments, effectiveness of the algorithm was verified. Basedon this, immune factors were added to optimize the algorithm. Simulation resultsshowed that ant colony algorithm performed remarkable precision in RNA secondarystructure prediction.The above research indicated that as a kind of swarm intelligence optimizationalgorithm, ant colony algorithm was effective for RNA secondary structure prediction.Our future research will further enhance the prediction precision and speed ofpseudoknots prediction algorithms.