Abstract
The secondary structure of RNA is closely related to its role in biological function, and it is difficult to predict the secondary structure of RNA sequence containing pseudoknots due to its complex structure. In this paper, a DT (Decision Tree) based RNA secondary structure prediction model is proposed, and a training algorithm is constructed. By adjusting the size of the window, the secondary structure prediction of RNA sequences containing pseudoknots is realized. The comparison experiments are carried out on the authoritative dataset RNA STRAND with classical classification algorithms such as LR (Logistic Regression), RF (Random forests) and SVM (Support Vector Machine). The experimental results show that compared with SVM, LR and RF, DT has more advantages in classification accuracy and robustness, and its accuracy rate has increased by 8.68%.
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Acknowledgements
This paper is supported by the National Natural Science Foundation of China (61772357, 61502329, 61672371, and 61876217), Jiangsu Province 333 Talent Project, Top Talent Project (DZXX-010), Suzhou Foresight Research Project (SYG201704, SNG201610, and SZS201609).
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Lu, W., Cao, Y., Wu, H., Huang, H., Ding, Y. (2019). Research on RNA Secondary Structure Prediction Based on Decision Tree. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_41
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DOI: https://doi.org/10.1007/978-3-030-26969-2_41
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