Application analysis of heuristic algorithms integrating dynamic programming in RNA secondary structure prediction

Ribonucleic acid is a crucial biomolecule in living organisms, with various types. To promote the research process of ribonucleic acid function, this study is for analyzing the utilization of heuristic algorithms on the ground of fusion dynamic programming in predicting the secondary structure of ribonucleic acid. Research on novel use of tree models for RNA secondary structure comparison, and use heuristic algorithms to optimize the multi branch structure comparison of tree models. Firstly, this study utilized dynamic programming algorithms to construct a comparison matrix and successfully found the backtracking path in the matrix. Meanwhile, for ensuring that the structural information of ribonucleic acid is not lost during the comparative analysis process, the study applies the idea of heuristic algorithms to calculate the optimal comparison between multi branched loops. Finally, the weights are adjusted using neural network algorithms to predict the optimal alignment structure. The results showed that the fusion dynamic programming heuristic algorithm achieved generalization performance of 0.928, 0.856, 0.842, and 0.793 on the target base data test sets of humans, mice, yeast, and spotted fish, respectively. Compared with the SimTree algorithm, the generalization performance has been improved by 15.13 %, 27.38 %, 27.77 %, and 38.88 %, respectively. In summary, the application of heuristic algorithms integrating dynamic programming in predicting the secondary structure of ribonucleic acid has good predictive performance. This has reference value for a deeper understanding of the structure and function relationship of ribonucleic acid.