A deep learning model for plant lncRNA-protein interaction prediction with graph attention

• Published in: Molecular genetics and genomics : MGG Vol. 295; no. 5; pp. 1091 - 1102
• Main Authors: Wekesa, Jael Sanyanda, Meng, Jun, Luan, Yushi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: 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; Deep learning; lncRNA; Graph attention; Interaction; Protein; Prediction
• ISSN: 1617-4615; 1617-4623; 1617-4623
• DOI: 10.1007/s00438-020-01682-w

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.