Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs

Author:

Xuan Ping1,Gong Zhe1,Cui Hui2,Li Bochong3,Zhang Tiangang4

Affiliation:

1. School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China

2. Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia

3. Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan

4. School of Mathematical Science, Heilongjiang University, Harbin 150080, China

Abstract

Abstract Since abnormal expression of long noncoding RNAs (lncRNAs) is often closely related to various human diseases, identification of disease-associated lncRNAs is helpful for exploring the complex pathogenesis. Most of recent methods concentrate on exploiting multiple kinds of data related to lncRNAs and diseases for predicting candidate disease-related lncRNAs. These methods, however, failed to deeply integrate the topology information from the meta-paths that are composed of lncRNA, disease and microRNA (miRNA) nodes. We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates. A heterogeneous graph that consists of lncRNA, disease and miRNA nodes were firstly constructed to integrate similarities, associations and interactions among them. Fully connected autoencoder-based module was established to extract the low-dimensional features of lncRNA, disease and miRNA nodes in the heterogeneous graph. We designed the attention mechanisms at the node feature level and at the meta-path level to learn more informative features and meta-paths. A module based on convolutional neural networks was constructed to encode the local topologies of lncRNA and disease nodes from multiple meta-path perspectives. The comprehensive experimental results demonstrated ACLDA achieves superior performance than several state-of-the-art prediction methods. Case studies on breast, lung and colon cancers demonstrated that ACLDA is able to discover the potential disease-related lncRNAs.

Funder

Natural Science Foundation of China

Heilongjiang Provincial Natural Science Foundation of China

China Postdoctoral Science Foundation

Heilongjiang Postdoctoral Scientific Research Staring Foundation

Innovation Talents Project of Harbin Science and Technology Bureau

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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