A deep learning model to predict RNA-Seq expression of tumours from whole slide images
RNA-sequencing of tumour tissue can provide important diagnostic and prognostic information but this is costly and not routinely performed in all clinical settings. Here, the authors show that whole slide histology slides—part of routine care—can be used to predict RNA-sequencing data and thus reduc...
Saved in:
Main Authors: | Benoît Schmauch, Alberto Romagnoni, Elodie Pronier, Charlie Saillard, Pascale Maillé, Julien Calderaro, Aurélie Kamoun, Meriem Sefta, Sylvain Toldo, Mikhail Zaslavskiy, Thomas Clozel, Matahi Moarii, Pierre Courtiol, Gilles Wainrib |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2020
|
Subjects: | |
Online Access: | https://doaj.org/article/d4db17b3e50d44079b75aaf30a53561e |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences.
by: Matahi Moarii, et al.
Published: (2014) -
Sliding of coherent twin boundaries
by: Zhang-Jie Wang, et al.
Published: (2017) -
Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
by: Bernd Lahrmann, et al.
Published: (2013) -
Scaling theory of rubber sliding friction
by: Reinhard Hentschke, et al.
Published: (2021) -
Transcription activation by a sliding clamp
by: Jing Shi, et al.
Published: (2021)