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

Abstract 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 ha...

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Bibliographic Details
Main Authors: Zhuangwei Shi, Han Zhang, Chen Jin, Xiongwen Quan, Yanbin Yin
Format: Article
Language:English
Published: BMC 2021-03-01
Series:BMC Bioinformatics
Subjects:
Variational inference
Graph autoencoder
lncRNA-disease association
Representation learning
Online Access:https://doi.org/10.1186/s12859-021-04073-z

Internet

https://doi.org/10.1186/s12859-021-04073-z

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