Detecting repeated cancer evolution from multi-region tumor sequencing data
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84668932
- Type
- D - Journal article
- DOI
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10.1038/s41592-018-0108-x
- Title of journal
- Nature Methods
- Article number
- -
- First page
- 707
- Volume
- 15
- Issue
- 9
- ISSN
- 1548-7091
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
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B - Data Science and Artificial Intelligence
- Citation count
- 41
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper develops a transfer-learning approach to infer cancer evolution from genetic patient samples, resulting in more accurate and predictive stratification of patients. It attracted considerable attention, with several articles in the general media (BBC, Independent, ...) and was nominated for Breakthrough of the Year by the magazine Physics World. The method is now being used by groups at the Crick Institute, and resulted in a keynote invitation to the 25th Biennial Meeting of the European Association of Cancer Research to joint corresponding author Dr Sottoriva.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -