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Deep-learning technology could help scientists to develop personalized immunotherapies and vaccines

Deep-learning technology developed by a team of Johns Hopkins engineers and cancer researchers can accurately predict cancer-related protein fragments that may trigger an immune system response. ....

United States , San Diego , Benjamin Alexander Albert , Johns Hopkins , Valsamo Elsa Anagnostou , Rachel Karchin , Danielle Ellis , Dipika Singh , Yunxiao Yang , Kellie Smith , Astra Zeneca , Xiaoshan Shao , Bloomberg Kimmel Institute For Cancer Immunotherapy , University Of California , National Institutes Of Health , Bristol Myers Squibb , Kimmel Cancer Center , Institute For Computational Medicine , Johns Hopkins Institute For Computational Medicine , Johns Hopkins Kimmel Cancer Center , Thoracic Malignancies Integrated Translational Science Center , Johns Hopkins University , Nature Machine Intelligence , Johns Hopkins Biomedical Engineering , Johns Hopkins Institute , Computational Medicine ,

Hard To Lose' Mutations In Tumors May Predict Response To Immunotherapy

Hard To Lose' Mutations In Tumors May Predict Response To Immunotherapy
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Noord Holland , Tanguy Seiwert , Christine Hann , Vincent Lam , Noushin Niknafs , Johns Hopkins , Karlijn Hummelink , Kellie Smith , Kim Monkhorst , Joseph Murray , Valsamo Anagnostou , Rachel Karchin , Julie Brahmer , Josephine Feliciano , Archana Balan , Patrick Forde , Xiaoshan Shao , Benjamin Levy , Victor Velculescu , Christina Kostandi , Kristen Marrone , Johns Hopkins University , Bloomberg Kimmel Institute For Cancer Immunotherapy , National Institutes Of Health , Lung Cancer Precision Medicine Center , Lungevity Foundation ,