Cargando…
Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression
BACKGROUND: In human genomes, long non-coding RNAs (lncRNAs) have attracted more and more attention because their dysfunctions are involved in many diseases. However, the associations between lncRNAs and diseases (LDA) still remain unknown in most cases. While identifying disease-related lncRNAs in...
Autores principales: | Shi, Jian-Yu, Huang, Hua, Zhang, Yan-Ning, Long, Yu-Xi, Yiu, Siu-Ming |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5763297/ https://www.ncbi.nlm.nih.gov/pubmed/29322937 http://dx.doi.org/10.1186/s12920-017-0305-y |
Ejemplares similares
-
TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs
por: Shi, Jian-Yu, et al.
Publicado: (2018) -
Random distributed logistic regression framework for predicting potential lncRNA‒disease association
por: Sun, Yichen, et al.
Publicado: (2021) -
Computation of elementary modes: a unifying framework and the new binary approach
por: Gagneur, Julien, et al.
Publicado: (2004) -
DeepWalk based method to predict lncRNA-miRNA associations via lncRNA-miRNA-disease-protein-drug graph
por: Yang, Long, et al.
Publicado: (2022) -
A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
por: Ye, Qing, et al.
Publicado: (2021)