Cargando…
Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA–disease association prediction
BACKGROUND: A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging tas...
Autores principales: | Huang, Dan, An, JiYong, Zhang, Lei, Liu, BaiLong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316361/ https://www.ncbi.nlm.nih.gov/pubmed/35879658 http://dx.doi.org/10.1186/s12859-022-04843-3 |
Ejemplares similares
-
Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model
por: Zhang, Lei, et al.
Publicado: (2020) -
Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction
por: Zhu, Rongxiang, et al.
Publicado: (2020) -
Predicting miRNA-disease associations via layer attention graph convolutional network model
por: Han, Han, et al.
Publicado: (2022) -
GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
por: Li, Lei, et al.
Publicado: (2021) -
Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition
por: Ni, Jie, et al.
Publicado: (2022)