Cargando…
scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics
Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data....
Autores principales: | Song, Qianqian, Su, Jing, Zhang, Wei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219725/ https://www.ncbi.nlm.nih.gov/pubmed/34158507 http://dx.doi.org/10.1038/s41467-021-24172-y |
Ejemplares similares
-
MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis
por: Li, Xiao, et al.
Publicado: (2022) -
Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN)
por: Lin, Huizhong, et al.
Publicado: (2023) -
GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels
por: Wen, Jie, et al.
Publicado: (2020) -
CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction
por: Zeng, Daojian, et al.
Publicado: (2021) -
CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition
por: Jia, Jingjing, et al.
Publicado: (2022)