Cargando…
Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network
There is a growing body of evidence indicating the crucial roles that long non-coding RNAs (lncRNAs) play in the development and progression of various diseases, including cancers, cardiovascular diseases, and neurological disorders. However, accurately predicting potential lncRNA-disease associatio...
Autores principales: | Lu, Zhonghao, Zhong, Hua, Tang, Lin, Luo, Jing, Zhou, Wei, Liu, Lin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10686445/ https://www.ncbi.nlm.nih.gov/pubmed/38019786 http://dx.doi.org/10.1371/journal.pcbi.1011634 |
Ejemplares similares
-
Association filtering and generative adversarial networks for predicting lncRNA-associated disease
por: Zhong, Hua, et al.
Publicado: (2023) -
Heterogeneous graph neural network for lncRNA-disease association prediction
por: Shi, Hong, et al.
Publicado: (2022) -
Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations
por: Xuan, Ping, et al.
Publicado: (2019) -
BiGAN: LncRNA-disease association prediction based on bidirectional generative adversarial network
por: Yang, Qiang, et al.
Publicado: (2021) -
Derivative-free optimization adversarial attacks for graph convolutional networks
por: Yang, Runze, et al.
Publicado: (2021)