This paper proposes algorithm in predicting the RNA secondary structure that combines several sequence comparisons, searches the eigenvalue for subsequence division with dynamic programing, utilizing the minimum free energy method. Moreover, the paper assesses the results derived from this new algorithm based on base-pairs distance, climbing distance and morphology distance. The paper also compares the assessment result and the prediction results of different prediction tools, and analyzes the advantages of the new method and its improvement direction.
Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.