scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene–gene interactions

Background Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human being...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	BMC genomics Ročník 25; číslo 1; s. 423 - 12
Hlavní autori:	Zhang, Wei, Yu, Ruochen, Xu, Zeqi, Li, Junnan, Gao, Wenhao, Jiang, Mingfeng, Dai, Qi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 29.04.2024 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Animal Genetics and Genomics Artificial neural networks Biomedical and Life Sciences Cell differentiation Cells Cluster Analysis Clustering Coefficient compression Computational Biology - methods Cross correlation Data compression Deep Learning Differentiation (biology) Dual-channel self-attention mechanism Epistasis, Genetic Gene expression Gene Expression Profiling - methods Gene Regulatory Networks Gene sequencing Genes Genetic aspects Genetic research Humans Life Sciences Methods Microarrays Microbial Genetics and Genomics Molecular modelling Neural networks Neural Networks, Computer Plant Genetics and Genomics Probability distribution Proteomics Regression analysis RNA RNA sequencing Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-cell RNA sequencing (scRNA-seq) Static gene–gene interactions Temporal variations Topology Transcriptomics China Single-cell RNA sequencing (scRNA-seq) Static gene–gene interactions Dual-channel self-attention mechanism Coefficient compression
ISSN:	1471-2164, 1471-2164
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!