A Hybrid Prediction Method for Plant lncRNA-Protein Interaction

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...

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Bibliographic Details
Published in:Cells (Basel, Switzerland) Vol. 8; no. 6; p. 521
Main Authors: Wekesa, Jael Sanyanda, Luan, Yushi, Chen, Ming, Meng, Jun
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 30.05.2019
MDPI
Subjects:
Algorithms
Arabidopsis - genetics
Arabidopsis thaliana
Artificial intelligence
autoencoder
Bioinformatics
Compression
Computational Biology - methods
Deep learning
Deoxyribonucleic acid
DNA
Gene expression
Gene regulation
hybrid
International conferences
Learning algorithms
light gradient boosting machine
lncRNA-protein interaction
Localization
Machine learning
Neural networks
plant
Plant diseases
Plant Proteins - metabolism
Prediction models
Protein Binding
random forest
Research methodology
RNA, Long Noncoding - genetics
RNA, Long Noncoding - metabolism
RNA-protein interactions
ROC Curve
Zea mays
Zea mays - genetics
United States > US
China
autoencoder
hybrid
random forest
light gradient boosting machine
lncRNA-protein interaction
plant
ISSN:2073-4409, 2073-4409
Online Access:Get full text
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Summary: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.
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ISSN:2073-4409
2073-4409
DOI:10.3390/cells8060521