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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Vydáno v:	Cells (Basel, Switzerland) Ročník 8; číslo 6; s. 521
Hlavní autoři:	Wekesa, Jael Sanyanda, Luan, Yushi, Chen, Ming, Meng, Jun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 30.05.2019 MDPI
Témata:	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
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Abstract	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.
AbstractList	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. 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 -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 and 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 and respectively. PLRPIM predicts potential lncRNA-protein interaction pairs effectively, which can facilitate lncRNA related research including function prediction. 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.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.
Author	Wekesa, Jael Sanyanda Luan, Yushi Chen, Ming Meng, Jun
AuthorAffiliation	3 School of Bioengineering, Dalian University of Technology, Dalian 116023, Liaoning, China; luanyush@dlut.edu.cn 4 College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; mchen@zju.edu.cn 1 School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, Liaoning, China; jael@mail.dlut.edu.cn 2 Department of Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000-00200, Kenya
AuthorAffiliation_xml	– name: 1 School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, Liaoning, China; jael@mail.dlut.edu.cn – name: 2 Department of Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi 62000-00200, Kenya – name: 3 School of Bioengineering, Dalian University of Technology, Dalian 116023, Liaoning, China; luanyush@dlut.edu.cn – name: 4 College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; mchen@zju.edu.cn
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SubjectTerms	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
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Title	A Hybrid Prediction Method for Plant lncRNA-Protein Interaction
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