Cargando…
GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network
In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for...
Autores principales: | Duan, Tao, Kuang, Zhufang, Wang, Jiaqi, Ma, Zhihao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718797/ https://www.ncbi.nlm.nih.gov/pubmed/34977011 http://dx.doi.org/10.3389/fcell.2021.753027 |
Ejemplares similares
-
GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network
por: Wang, Jiaqi, et al.
Publicado: (2020) -
Metagraphs and their applications
por: Basu, Amit, et al.
Publicado: (2007) -
LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model
por: Wei, Meng-Meng, et al.
Publicado: (2023) -
CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
por: Ma, Zhihao, et al.
Publicado: (2021) -
KG2Vec: A node2vec-based vectorization model for knowledge graph
por: Wang, YueQun, et al.
Publicado: (2021)