A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.
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Nature Portfolio
2020
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oai:doaj.org-article:1eb66053b1d54801a9b93093526fbf222021-12-02T17:31:54ZA deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns10.1038/s41467-019-13825-82041-1723https://doaj.org/article/1eb66053b1d54801a9b93093526fbf222020-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13825-8https://doaj.org/toc/2041-1723Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.Wei JiaoGurnit AtwalPaz PolakRosa KarlicEdwin CuppenPCAWG Tumor Subtypes and Clinical Translation Working GroupAlexandra DanyiJeroen de RidderCarla van HerpenMartijn P. LolkemaNeeltje SteeghsGad GetzQuaid MorrisLincoln D. SteinPCAWG ConsortiumNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q Wei Jiao Gurnit Atwal Paz Polak Rosa Karlic Edwin Cuppen PCAWG Tumor Subtypes and Clinical Translation Working Group Alexandra Danyi Jeroen de Ridder Carla van Herpen Martijn P. Lolkema Neeltje Steeghs Gad Getz Quaid Morris Lincoln D. Stein PCAWG Consortium A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| description |
Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data. |
| format |
article |
| author |
Wei Jiao Gurnit Atwal Paz Polak Rosa Karlic Edwin Cuppen PCAWG Tumor Subtypes and Clinical Translation Working Group Alexandra Danyi Jeroen de Ridder Carla van Herpen Martijn P. Lolkema Neeltje Steeghs Gad Getz Quaid Morris Lincoln D. Stein PCAWG Consortium |
| author_facet |
Wei Jiao Gurnit Atwal Paz Polak Rosa Karlic Edwin Cuppen PCAWG Tumor Subtypes and Clinical Translation Working Group Alexandra Danyi Jeroen de Ridder Carla van Herpen Martijn P. Lolkema Neeltje Steeghs Gad Getz Quaid Morris Lincoln D. Stein PCAWG Consortium |
| author_sort |
Wei Jiao |
| title |
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| title_short |
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| title_full |
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| title_fullStr |
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| title_full_unstemmed |
A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| title_sort |
deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/1eb66053b1d54801a9b93093526fbf22 |
| work_keys_str_mv |
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