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

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 l...

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Vydáno v:Briefings in bioinformatics Ročník 23; číslo 3
Hlavní autoři: Xuan, Ping, Gong, Zhe, Cui, Hui, Li, Bochong, Zhang, Tiangang
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 13.05.2022
Oxford Publishing Limited (England)
Témata:
Artificial neural networks
Breast
Case studies
Colon
Disease
Feature extraction
Information processing
MicroRNAs
miRNA
Modules
Network topologies
Neural networks
Nodes
Non-coding RNA
Pathogenesis
Ribonucleic acid
RNA
Topology
meta-path level attention mechanism
node feature level attention mechanism
lncRNA-disease association prediction
low-dimensional representation learning
topology learning based on meta-paths
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí: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.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac089