A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been a...

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Veröffentlicht in:BMC bioinformatics Jg. 22; H. 1; S. 136 - 20
Hauptverfasser: Shi, Zhuangwei, Zhang, Han, Jin, Chen, Quan, Xiongwen, Yin, Yanbin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 21.03.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Alzheimer's disease
Bioinformatics
Biomedical and Life Sciences
Breast cancer
Case studies
Computational Biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data mining
Datasets
Deep learning
Genes
Graph autoencoder
Graphic methods
Graphical representations
Health aspects
Humans
Inference
Learning algorithms
Life Sciences
lncRNA-disease association
Machine Learning
Medical genetics
Medical research
Medicine, Experimental
Methods
Microarrays
Model testing
Neural networks
Non-coding RNA
Performance evaluation
Propagation
Representation learning
Research Article
RNA
RNA, Long Noncoding - genetics
Robustness (mathematics)
Software
Source code
Training
Variational inference
China
Representation learning
Graph autoencoder
Variational inference
lncRNA-disease association
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA .
Bibliographie:ObjectType-Article-1
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-021-04073-z