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Open AccessArticle

A Hybrid Prediction Method for Plant lncRNA-Protein Interaction

1
School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, Liaoning, China
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Department of Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000-00200, Kenya
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School of Bioengineering, Dalian University of Technology, Dalian 116023, Liaoning, China
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College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
*
Author to whom correspondence should be addressed.
Cells 2019, 8(6), 521; https://doi.org/10.3390/cells8060521
Received: 26 April 2019 / Revised: 22 May 2019 / Accepted: 29 May 2019 / Published: 30 May 2019
Long non-protein-coding RNAs (lncRNAs) identification and analysis are pervasive in transcriptome studies due to their roles in biological processes. In particular, lncRNA-protein interaction has plausible relevance to gene expression regulation and in cellular processes such as pathogen resistance in plants. While lncRNA-protein interaction has been studied in animals, there has yet to be extensive research in plants. In this paper, we propose a novel plant lncRNA-protein interaction prediction method, namely PLRPIM, which combines deep learning and shallow machine learning methods. The selection of an optimal feature subset and subsequent efficient compression are significant challenges for deep learning models. The proposed method adopts k-mer and extracts high-level abstraction sequence-based features using stacked sparse autoencoder. Based on the extracted features, the fusion of random forest (RF) and light gradient boosting machine (LGBM) is used to build the prediction model. The performances are evaluated on Arabidopsis thaliana and Zea mays datasets. Results from experiments demonstrate PLRPIM’s superiority compared with other prediction tools on the two datasets. Based on 5-fold cross-validation, we obtain 89.98% and 93.44% accuracy, 0.954 and 0.982 AUC for Arabidopsis thaliana and Zea mays, respectively. PLRPIM predicts potential lncRNA-protein interaction pairs effectively, which can facilitate lncRNA related research including function prediction. View Full-Text
Keywords: autoencoder; random forest; light gradient boosting machine; hybrid; lncRNA-protein interaction; plant autoencoder; random forest; light gradient boosting machine; hybrid; lncRNA-protein interaction; plant
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Wekesa, J.S.; Luan, Y.; Chen, M.; Meng, J. A Hybrid Prediction Method for Plant lncRNA-Protein Interaction. Cells 2019, 8, 521.

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