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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!