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...
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Nature Portfolio
2020
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oai:doaj.org-article:d4db17b3e50d44079b75aaf30a53561e2021-12-02T18:49:31ZA deep learning model to predict RNA-Seq expression of tumours from whole slide images10.1038/s41467-020-17678-42041-1723https://doaj.org/article/d4db17b3e50d44079b75aaf30a53561e2020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17678-4https://doaj.org/toc/2041-1723RNA-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 reduce the need for additional analyses.Benoît SchmauchAlberto RomagnoniElodie PronierCharlie SaillardPascale MailléJulien CalderaroAurélie KamounMeriem SeftaSylvain ToldoMikhail ZaslavskiyThomas ClozelMatahi MoariiPierre CourtiolGilles WainribNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-15 (2020) |
| institution |
DOAJ |
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DOAJ |
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EN |
| topic |
Science Q |
| spellingShingle |
Science Q 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 A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| description |
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 reduce the need for additional analyses. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Benoît Schmauch |
| title |
A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| title_short |
A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| title_full |
A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| title_fullStr |
A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| title_full_unstemmed |
A deep learning model to predict RNA-Seq expression of tumours from whole slide images |
| title_sort |
deep learning model to predict rna-seq expression of tumours from whole slide images |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/d4db17b3e50d44079b75aaf30a53561e |
| work_keys_str_mv |
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