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

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...

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Bibliographic Details
Main Authors: Jin, C. (Author), Quan, X. (Author), Shi, Z. (Author), Yin, Y. (Author), Zhang, H. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
biology
Computational Biology
Deep learning
Diagnosis
Disease associations
Disease control
Expectation-maximization algorithms
Forecasting
genetics
Graph autoencoder
Graph representation
High dimensional feature
human
Humans
Image segmentation
Learning algorithms
Learning systems
lncRNA-disease association
long untranslated RNA
Low-dimensional representation
machine learning
Machine Learning
Maximum principle
Numerical experiments
Numerical methods
Representation learning
RNA, Long Noncoding
software
Software
State-of-the-art methods
Variational inference
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