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CIR Toolbox Coding and Computation

This collection is part of the Cancer Immunology Research editors’ “Toolbox” series, which highlights the most interesting platforms, methodologies, and prediction models recently published in the journal. In this collection, we have included articles that utilize machine learning algorithms or other computational methods to improve prediction modeling and identification of novel neoantigens, peptides, and cell types. We hope you enjoy this collection!

Special Collection Image
Christof C. Smith; Shengjie Chai; Amber R. Washington; Samuel J. Lee; Elisa Landoni; Kevin Field; Jason Garness; Lisa M. Bixby; Sara R. Selitsky; Joel S. Parker; Barbara Savoldo; Jonathan S. Serody; Benjamin G. Vincent
Maria Bonsack; Stephanie Hoppe; Jan Winter; Diana Tichy; Christine Zeller; Marius D. Küpper; Eva C. Schitter; Renata Blatnik; Angelika B. Riemer
Ines P. Nearchou; Kate Lillard; Christos G. Gavriel; Hideki Ueno; David J. Harrison; Peter D. Caie
Juliane Liepe; John Sidney; Felix K.M. Lorenz; Alessandro Sette; Michele Mishto
Allison R. Greenplate; Daniel D. McClanahan; Brian K. Oberholtzer; Deon B. Doxie; Caroline E. Roe; Kirsten E. Diggins; Nalin Leelatian; Megan L. Rasmussen; Mark C. Kelley; Vivian Gama; Peter J. Siska; Jeffrey C. Rathmell; P. Brent Ferrell; Douglas B. Johnson; Jonathan M. Irish
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