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A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

Danh-Tai Hoang, Gal Dinstag, Eldad D. Shulman, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Stephen‐John Sammut, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Chani Stossel, Tejas Patil, Arun Rajan, Wiem Lassoued, Julius Strauss, Shania Bailey, Clint Allen, Jason M. Redman, Tuvik Beker, Peng Jiang, Talia Golan, Scott Wilkinson, Adam G. Sowalsky, Sharon R. Pine, Carlos Caldas, James L. Gulley, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, Eytan Ruppin

2024Nature Cancer136 citationsDOIOpen Access PDF

Topics & Concepts

TranscriptomeArtificial intelligenceDeep learningComputer scienceDigital pathologyMedicineCancerMachine learningOncologyInternal medicineGene expressionBiologyGeneBiochemistryAI in cancer detectionCancer Genomics and DiagnosticsCell Image Analysis Techniques
A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics | Litcius