BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies
- Submitting institution
-
University of Glasgow
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-04727
- Type
- D - Journal article
- DOI
-
10.1186/s13059-015-0592-6
- Title of journal
- Genome Biology
- Article number
- -
- First page
- 36
- Volume
- 16
- Issue
- 1
- ISSN
- 1465-6906
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
http://eprints.gla.ac.uk/129183/
- 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
-
3
- Research group(s)
-
-
- Citation count
- 54
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- ORIGINALITY: A novel class of Bayesian nonparametric models that enables highly automatic and interpretable unsupervised learning to solve challenges in studying cancer evolution. SIGNIFICANCE: Demonstrates the effectiveness of integrating Bayesian nonparametric and phylogenetics models. The method has been very well cited, and considered as a baseline in dozens of benchmarking studies and connected to reveal genetic diversities of coronavirus in patient samples. Described as “an exciting advance in the development and implementation of tumour phylogeny methods” in Nature Review Genetics paper. RIGOUR: The method is tested extensively with simulated data and two different types of data in two cancer types.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
