Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

Weijun Zhu; Xiaokai Liu; Mingliang Xu; Huanmei Wu

doi:10.1109/JAS.2019.1911756

Volume 6 Issue 6

Nov. 2019

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(6): 1384-1396

Weijun Zhu, Xiaokai Liu, Mingliang Xu and Huanmei Wu, "Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1384-1396, Nov. 2019. doi: 10.1109/JAS.2019.1911756

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Weijun Zhu, Xiaokai Liu, Mingliang Xu and Huanmei Wu, "Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning," IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1384-1396, Nov. 2019. doi: 10.1109/JAS.2019.1911756

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Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

doi: 10.1109/JAS.2019.1911756

Funds: This work was supported by the National Natural Science Foundation of China (U1204608, 61472370, 61672469, 61822701)

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Abstract

Abstract

Ribonucleic acid (RNA) hybridization is widely used in popular RNA simulation software in bioinformatics. However, limited by the exponential computational complexity of combinatorial problems, it is challenging to decide, within an acceptable time, whether a specific RNA hybridization is effective. We hereby introduce a machine learning based technique to address this problem. Sample machine learning (ML) models tested in the training phase include algorithms based on the boosted tree (BT), random forest (RF), decision tree (DT) and logistic regression (LR), and the corresponding models are obtained. Given the RNA molecular coding training and testing sets, the trained machine learning models are applied to predict the classification of RNA hybridization results. The experiment results show that the optimal predictive accuracies are 96.2%, 96.6%, 96.0% and 69.8% for the RF, BT, DT and LR-based approaches, respectively, under the strong constraint condition, compared with traditional representative methods. Furthermore, the average computation efficiency of the RF, BT, DT and LR-based approaches are 208 679, 269 756, 184 333 and 187 458 times higher than that of existing approach, respectively. Given an RNA design, the BT-based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effectiveness of molecular hybridization.
- Biological effectiveness,
- boosted tree (BT),
- decision tree (DT),
- random forest (RF),
- RNA design,
- specific hybridization,
- logistic regression (LR)

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Highlights

The RNA hybridization is one of the most important biochemical reactions.
How to compute RNA hybridizations in simulation software? It suffers from exponential complexity.
The above problem is reduced to a binary classification problem of machine learning in this paper.
As a result, the above problem has a polynomial approximate solution.
The new method is 269756 times faster than state of the art of RNA hybridization algorithm.

Predicting the Results of RNA Molecular Specific Hybridization Using Machine Learning

doi: 10.1109/JAS.2019.1911756

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通讯作者: 陈斌, bchen63@163.com

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