Cargando…
Predicting miRNA-disease associations using a hybrid feature representation in the heterogeneous network
BACKGROUND: Studies have found that miRNAs play an important role in many biological activities involved in human diseases. Revealing the associations between miRNA and disease by biological experiments is time-consuming and expensive. The computational approaches provide a new alternative. However,...
Autores principales: | Liu, Minghui, Yang, Jingyi, Wang, Jiacheng, Deng, Lei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579981/ https://www.ncbi.nlm.nih.gov/pubmed/33087118 http://dx.doi.org/10.1186/s12920-020-00783-0 |
Ejemplares similares
-
Prediction of potential small molecule−miRNA associations based on heterogeneous network representation learning
por: Li, Jianwei, et al.
Publicado: (2022) -
TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network
por: Yang, Yi, et al.
Publicado: (2022) -
Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks
por: Ji, Cunmei, et al.
Publicado: (2021) -
MiRNA–miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features
por: Xu, Juan, et al.
Publicado: (2011) -
DRMDA: deep representations‐based miRNA–disease association prediction
por: Chen, Xing, et al.
Publicado: (2017)