Multiple types of disease-associated RNAs identification for disease prognosis and therapy using heterogeneous graph learning
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11432-024-4100-7.pdf
Reference5 articles.
1. Li Q, Gloudemans M J, Geisinger J M, et al. RNA editing underlies genetic risk of common inflammatory diseases. Nature, 2022, 608: 569–577
2. Nemeth K, Bayraktar R, Ferracin M, et al. Non-coding RNAs in disease: from mechanisms to therapeutics. Nat Rev Genet, 2024, 25: 211–232
3. Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst, 2020, 32: 4–24
4. Lu Z, Zhong H, Tang L, et al. Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network. PLoS Comput Biol, 2023, 19: e1011634
5. Guo Y, Zhou D, Ruan X, et al. Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features. Neural Networks, 2023, 165: 491–505
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