Cargando…
MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data
Advancements in single-cell sequencing research have revolutionized our understanding of cellular heterogeneity and functional diversity through the analysis of single-cell transcriptomes and genomes. A crucial step in single-cell RNA sequencing (scRNA-seq) analysis is identifying cell types. Howeve...
Autores principales: | Jiao, Linfang, Ren, Yongqi, Wang, Lulu, Gao, Changnan, Wang, Shuang, Song, Tao |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10110861/ https://www.ncbi.nlm.nih.gov/pubmed/37082202 http://dx.doi.org/10.3389/fgene.2023.1179859 |
Ejemplares similares
-
TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer
por: Song, Tao, et al.
Publicado: (2022) -
SMIXnorm: Fast and Accurate RNA-Seq Data Normalization for Formalin-Fixed Paraffin-Embedded Samples
por: Yin, Shen, et al.
Publicado: (2021) -
SAAED: Embedding and Deep Learning Enhance Accurate Prediction of Association Between circRNA and Disease
por: Liu, Qingyu, et al.
Publicado: (2022) -
Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq
por: Cui, Zhe, et al.
Publicado: (2021) -
An Efficient Computational Model for Large-Scale Prediction of Protein–Protein Interactions Based on Accurate and Scalable Graph Embedding
por: Su, Xiao-Rui, et al.
Publicado: (2021)