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CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations
Open AccessArticle

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs

1
School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
2
Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
3
School of Mathematical Science, Heilongjiang University, Harbin 150080, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(18), 4458; https://doi.org/10.3390/ijms20184458
Received: 22 June 2019 / Revised: 5 September 2019 / Accepted: 6 September 2019 / Published: 10 September 2019
(This article belongs to the Special Issue Special Protein or RNA Molecules Computational Identification 2019)
Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA–disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA–disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA–disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred’s ability to discover potential lncRNA–disease associations. View Full-Text
Keywords: lncRNA–disease association; information flow propagation; network topological structure; convolutional neural network; deep learning lncRNA–disease association; information flow propagation; network topological structure; convolutional neural network; deep learning
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Xuan, P.; Jia, L.; Zhang, T.; Sheng, N.; Li, X.; Li, J. LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs. Int. J. Mol. Sci. 2019, 20, 4458.

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