Abstract
Long non-coding RNA (lncRNA) plays an important role in regulating biological activities. Traditional feature engineering methods for lncRNA prediction rely on prior experience and require manual feature extraction from some related datasets. Besides, the structure of plant lncRNA is complex. It is difficult to extract features with good discrimination. This paper proposes a method based on long short-term memory networks (LSTM) for lncRNA recognition called lncRNA-LSTM. K-means clustering is used to solve the problem of unbalanced sample size at first, p-nts coding is performed according to the characteristics of RNA sequences, and it is input into a recurrent neural network including embedded layer, LSTM layer and full connection layer. lncRNA-LSTM is more effective than support vector machine, Naive Bayes and other model with feature fusing of open reading frame, second structure and k-mers. Using the same Zea mays dataset, lncRNA-LSTM achieves 96.2% accuracy which is 0.053, 0.173, 0.211 and 0.162 higher than that of CPC2, CNCI, PLEK and LncADeep, the precision and recall are much more effective and robust.
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Acknowledgment
The current study was supported by the National Natural Science Foundation of China (Nos. 61872055 and 31872116).
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Meng, J., Chang, Z., Zhang, P., Shi, W., Luan, Y. (2019). lncRNA-LSTM: Prediction of Plant Long Non-coding RNAs Using Long Short-Term Memory Based on p-nts Encoding. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_32
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DOI: https://doi.org/10.1007/978-3-030-26766-7_32
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