Cargando…
Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model
In recent years, accumulating evidences have shown that microRNA (miRNA) plays an important role in the exploration and treatment of diseases, so detection of the associations between miRNA and disease has been drawn more and more attentions. However, traditional experimental methods have the limita...
Autores principales: | Ji, Bo-Ya, You, Zhu-Hong, Cheng, Li, Zhou, Ji-Ren, Alghazzawi, Daniyal, Li, Li-Ping |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7170854/ https://www.ncbi.nlm.nih.gov/pubmed/32313121 http://dx.doi.org/10.1038/s41598-020-63735-9 |
Ejemplares similares
-
Prediction of lncRNA-disease associations via an embedding learning HOPE in heterogeneous information networks
por: Zhou, Ji-Ren, et al.
Publicado: (2020) -
NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information
por: Ji, Bo-Ya, et al.
Publicado: (2020) -
DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations
por: Zheng, Kai, et al.
Publicado: (2019) -
RepTar: a database of predicted cellular targets of host and viral miRNAs
por: Elefant, Naama, et al.
Publicado: (2011) -
Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction
por: Yu, Dong-Ling, et al.
Publicado: (2021)