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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Vydáno v:	BMC bioinformatics Ročník 22; číslo 1; s. 136 - 20
Hlavní autoři:	Shi, Zhuangwei, Zhang, Han, Jin, Chen, Quan, Xiongwen, Yin, Yanbin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 21.03.2021 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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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Popis
Shrnutí:	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 .
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04073-z