Cargando…
Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning
Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in lar...
Autores principales: | Rahaman, Md Mamunur, Millar, Ewan K. A., Meijering, Erik |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442349/ https://www.ncbi.nlm.nih.gov/pubmed/37604916 http://dx.doi.org/10.1038/s41598-023-40219-0 |
Ejemplares similares
-
hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images
por: Mondol, Raktim Kumar, et al.
Publicado: (2023) -
Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images
por: Sandarenu, Piumi, et al.
Publicado: (2022) -
TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics
por: Shan, Yiran, et al.
Publicado: (2022) -
Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images
por: Wang, Xiaoxiao, et al.
Publicado: (2021) -
A bird’s-eye view of deep learning in bioimage analysis
por: Meijering, Erik
Publicado: (2020)