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Received: 12 October 2018 Accepted: 28 January 2019 In recent years, with the demand for pattern information in the field of bioinformatics research, frequent subtree mining algorithms have become a research hotspot. In this paper, a fast and efficient mining algorithm based on frequent embedded subtree is proposed to solve the problem of large-scale of biological data and high sequence pattern similarity in the process of biological data mining. The algorithm uses a unique string coding method to represent the tree, and uses a scope-list for substring amplification and frequency testing. The pruning technique greatly compresses the search space and reduces the computational time. Compared with the classical pattern mining algorithm, the proposed algorithm improves the efficiency of mining greatly.

Frequent Subtree Mining Algorithm for Ribonucleic Acid Topological Pattern