Cargando…
A Computational Model to Predict the Causal miRNAs for Diseases
MicroRNAs (miRNAs) are one class of important noncoding RNA molecules, and their dysfunction is associated with a number of diseases. Currently, a series of databases and algorithms have been developed for dissecting human miRNA–disease associations. However, these tools only presented the associati...
Autores principales: | Gao, Yuanxu, Jia, Kaiwen, Shi, Jiangcheng, Zhou, Yuan, Cui, Qinghua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6786093/ https://www.ncbi.nlm.nih.gov/pubmed/31632446 http://dx.doi.org/10.3389/fgene.2019.00935 |
Ejemplares similares
-
Annotation and curation of the causality information in LncRNADisease
por: Jia, Kaiwen, et al.
Publicado: (2020) -
Benchmark of computational methods for predicting microRNA-disease associations
por: Huang, Zhou, et al.
Publicado: (2019) -
Predicting lncRNA–Protein Interactions With miRNAs as Mediators in a Heterogeneous Network Model
por: Zhou, Yuan-Ke, et al.
Publicado: (2020) -
LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations
por: Huang, Zhou, et al.
Publicado: (2021) -
sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers
por: Shi, Jiangcheng, et al.
Publicado: (2020)