Subclonal reconstruction of tumors by using machine learning and population genetics

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

NATURE GENETICS, cilt.52, sa.9, ss.898-919, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 9
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1038/s41588-020-0675-5
  • Dergi Adı: NATURE GENETICS
  • Derginin Tarandığı İndeksler: 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
  • Sayfa Sayıları: ss.898-919
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.