A deep learning model for plant lncRNA-protein interaction prediction with graph attention
Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LP...
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| Published in | Molecular genetics and genomics : MGG Vol. 295; no. 5; pp. 1091 - 1102 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1617-4615 1617-4623 1617-4623 |
| DOI | 10.1007/s00438-020-01682-w |
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| Abstract | Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and
l
2
-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on
Arabidopsis thaliana
and
Zea mays
datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. |
|---|---|
| AbstractList | Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l2-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods.Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l2-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l2-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l₂-norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l 2 -norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. Long non-coding RNAs (lncRNAs) play a broad spectrum of distinctive regulatory roles through interactions with proteins. However, only a few plant lncRNAs have been experimentally characterized. We propose GPLPI, a graph representation learning method, to predict plant lncRNA-protein interaction (LPI) from sequence and structural information. GPLPI employs a generative model using long short-term memory (LSTM) with graph attention. Evolutionary features are extracted using frequency chaos game representation (FCGR). Manifold regularization and l -norm are adopted to obtain discriminant feature representations and mitigate overfitting. The model captures locality preserving and reconstruction constraints that lead to better generalization ability. Finally, potential interactions between lncRNAs and proteins are predicted by integrating catboost and regularized Logistic regression based on L-BFGS optimization algorithm. The method is trained and tested on Arabidopsis thaliana and Zea mays datasets. GPLPI achieves accuracies of 85.76% and 91.97% respectively. The results show that our method consistently outperforms other state-of-the-art methods. |
| Author | Wekesa, Jael Sanyanda Luan, Yushi Meng, Jun |
| Author_xml | – sequence: 1 givenname: Jael Sanyanda surname: Wekesa fullname: Wekesa, Jael Sanyanda organization: School of Computer Science and Technology, Dalian University of Technology, School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology – sequence: 2 givenname: Jun orcidid: 0000-0002-7357-8562 surname: Meng fullname: Meng, Jun email: mengjun@dlut.edu.cn organization: School of Computer Science and Technology, Dalian University of Technology – sequence: 3 givenname: Yushi surname: Luan fullname: Luan, Yushi organization: School of Bioengineering, Dalian University of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32409904$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning lncRNA Graph attention Interaction Protein Prediction |
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| SubjectTerms | algorithms Amino acid sequence Animal Genetics and Genomics Arabidopsis thaliana Biochemistry Biomedical and Life Sciences data collection Deep learning Fc receptors genomics Graph representations Human Genetics Life Sciences Long short-term memory Microbial Genetics and Genomics neural networks Non-coding RNA Original Article Plant Genetics and Genomics prediction regression analysis Zea mays |
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| Title | A deep learning model for plant lncRNA-protein interaction prediction with graph attention |
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