Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonati...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Yuzhou, He, Fei, Wang, Juexin, Chen, Shuo, Li, Jingyi, Liu, Jixin, Yu, Yang, Su, Li, Ma, Anjun, Allen, Carter, Lin, Yu, Sun, Shaoli, Liu, Bingqiang, Javier Otero, José, Chung, Dongjun, Fu, Hongjun, Li, Zihai, Xu, Dong, Ma, Qin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440291/
https://www.ncbi.nlm.nih.gov/pubmed/36090815
http://dx.doi.org/10.1016/j.csbj.2022.08.029

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9440291/
https://www.ncbi.nlm.nih.gov/pubmed/36090815
http://dx.doi.org/10.1016/j.csbj.2022.08.029

Ejemplares similares