Inferring structural variant cancer cell fraction
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-08316
- Type
- D - Journal article
- DOI
-
10.1038/s41467-020-14351-8
- Title of journal
- Nature Communications
- Article number
- 730
- First page
- -
- Volume
- 11
- Issue
- -
- ISSN
- 2041-1723
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2020
- URL
-
http://eprints.gla.ac.uk/209662/
- 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
- Yes
- Number of additional authors
-
10
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ORIGINALITY: Presents a novel formulation of an unsupervised learning problem motivated by challenges in modelling cancer. The algorithm predicts which tumours are more dangerous. SIGNIFICANCE: part of a leading international project that studies the most comprehensive genomic data of cancer to date. Nature and Nature subjournals collectively published findings of this project, including this work. Co-first author role highlights computational SIGNIFICANCE. The method has been adopted by more than 10 research groups around the world. RIGOUR: The method is tested extensively with simulated data and real data. The scale of the real data is 100 times larger than previous attempts.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -