Cargando…
DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization
BACKGROUND: In the development of science and technology, there are increasing evidences that there are some associations between lncRNAs and human diseases. Therefore, finding these associations between them will have a huge impact on our treatment and prevention of some diseases. However, the proc...
Autores principales: | Liu, Jin-Xing, Gao, Ming-Ming, Cui, Zhen, Gao, Ying-Lian, Li, Feng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8114493/ https://www.ncbi.nlm.nih.gov/pubmed/33980147 http://dx.doi.org/10.1186/s12859-020-03868-w |
Ejemplares similares
-
MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations
por: Wu, Tian-Ru, et al.
Publicado: (2020) -
RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations
por: Cui, Zhen, et al.
Publicado: (2019) -
lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering
por: Wang, Bo, et al.
Publicado: (2022) -
NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations
por: Gao, Ying-Lian, et al.
Publicado: (2019) -
LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation
por: Ou-Yang, Le, et al.
Publicado: (2019)