Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single...

Descripción completa

Detalles Bibliográficos
Autores principales: Xi, Nan Miles, Li, Jingyi Jessica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475479/
https://www.ncbi.nlm.nih.gov/pubmed/37671239
http://dx.doi.org/10.1016/j.csbj.2023.07.041

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475479/
https://www.ncbi.nlm.nih.gov/pubmed/37671239
http://dx.doi.org/10.1016/j.csbj.2023.07.041

Ejemplares similares