Subclonal reconstruction of tumors by using machine learning and population genetics

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Caravagna G., Heide T., Williams M. J., Zapata L., Nichol D., Chkhaidze K., ...More

NATURE GENETICS, vol.52, no.9, pp.898-919, 2020 (SCI-Expanded)

• Publication Type: Article / Article
• Volume: 52 Issue: 9
• Publication Date: 2020
• Doi Number: 10.1038/s41588-020-0675-5
• Journal Name: NATURE GENETICS
• Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, EMBASE, MEDLINE, Veterinary Science Database, DIALNET
• Page Numbers: pp.898-919
• Middle East Technical University Affiliated: Yes

Abstract

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.