Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

Adam J. Widman
, Minita Shah
, Amanda Frydendahl
, Daniel Halmos
, Cole C. Khamnei
, Nadia Øgaard
, Srinivas Rajagopalan
, Anushri Arora
, Aditya Deshpande
, William F. Hooper
, Jean Quentin
, Jake Bass
, Mingxuan Zhang
, Theophile Langanay
, Laura Andersen
, Zoe Steinsnyder
, Will Liao
, Mads Heilskov Rasmussen
, Tenna Vesterman Henriksen
, Sarah Østrup Jensen

Jesper Nors, Christina Therkildsen, Jesus Sotelo, Ryan Brand, Joshua S. Schiffman, Ronak H. Shah, Alexandre Pellan Cheng, Colleen Maher, Lavinia Spain, Kate Krause, Dennie T. Frederick, Wendie den Brok, Caroline Lohrisch, Tamara Shenkier, Christine Simmons, Diego Villa, Andrew J. Mungall, Richard Moore, Elena Zaikova, Viviana Cerda, Esther Kong, Daniel Lai, Murtaza S. Malbari, Melissa Marton, Dina Manaa, Lara Winterkorn, Karen Gelmon, Margaret K. Callahan, Genevieve Boland, Catherine Potenski, Jedd D. Wolchok, Ashish Saxena, Samra Turajlic, Marcin Imielinski, Michael F. Berger, Sam Aparicio, Nasser K. Altorki, Michael A. Postow, Nicolas Robine, Claus Lindbjerg Andersen, Dan A. Landau

New York Genome Cente
Memorial Sloan-Kettering Cancer Center
Aarhus University
Cornell University
University of Copenhagen
Parker Institute for Cancer Immunotherapy
The Francis Crick Institute
Royal Marsden NHS Foundation Trust
Massachusetts General Hospital Cancer Center
Provincial Health Services Authority
Canada's Michael Smith Genome Sciences Centre
New York University
University of British Columbia

Résultats de recherche: Contribution à un journal › Article publié dans une revue, révisé par les pairs › Revue par des pairs

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Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment

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Medicine and Dentistry

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