A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
Danh-Tai Hoang(Australian National University), Gal Dinstag, Eldad D. Shulman(National Cancer Institute), Leandro C. Hermida(University of Pittsburgh), Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley(Australian National University), Stephen‐John Sammut(Institute of Cancer Research), Sanju Sinha(National Cancer Institute), Neelam Sinha(National Cancer Institute), Christopher H. Dampier(National Cancer Institute), Chani Stossel(Tel Aviv University), Tejas Patil(University of Colorado Anschutz Medical Campus), Arun Rajan(National Cancer Institute), Wiem Lassoued(National Cancer Institute), Julius Strauss(National Cancer Institute), Shania Bailey(National Cancer Institute), Clint Allen(National Cancer Institute), Jason M. Redman(National Cancer Institute), Tuvik Beker, Peng Jiang(National Cancer Institute), Talia Golan(Tel Aviv University), Scott Wilkinson(National Cancer Institute), Adam G. Sowalsky(National Cancer Institute), Sharon R. Pine(University of Colorado Anschutz Medical Campus), Carlos Caldas(University of Cambridge), James L. Gulley(National Cancer Institute), Kenneth Aldape(National Cancer Institute), Ranit Aharonov, Eric A. Stone(Australian National University), Eytan Ruppin(National Cancer Institute)
Cited by 126Open Access
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