Subclonal reconstruction of tumors by using machine learning and population genetics

Date

2020-09-01

Author

Caravagna, Giulio
Heide, Timon
Williams, Marc J.
Zapata, Luis
Nichol, Daniel
Chkhaidze, Ketevan
Cross, William
Cresswell, George D.
Werner, Benjamin
Acar, Ahmet
Chesler, Louis
Barnes, Chris P.
Sanguinetti, Guido
Graham, Trevor A.
Sottoriva, Andrea

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MOBSTER is an approach for subclonal reconstruction of tumors from cancer genomics data on the basis of models that combine machine learning with evolutionary theory, thus leading to more accurate evolutionary histories of tumors.

URI

https://hdl.handle.net/11511/89494

Journal

NATURE GENETICS

DOI

https://doi.org/10.1038/s41588-020-0675-5

Collections

Department of Biology, Article

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Citation Formats

G. Caravagna et al., “Subclonal reconstruction of tumors by using machine learning and population genetics,” NATURE GENETICS, pp. 898–919, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/89494.