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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Bibliographic Details
Published in:	BMC bioinformatics Vol. 22; no. 1; pp. 136 - 20
Main Authors:	Shi, Zhuangwei, Zhang, Han, Jin, Chen, Quan, Xiongwen, Yin, Yanbin
Format:	Journal Article
Language:	English
Published:	London BioMed Central 21.03.2021 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	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
Online Access:	Get full text
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