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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!

Research Articles Author Choice
Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes
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
10.1158/2326-6066.CIR-19-0155
Research Articles
Maria Bonsack; Stephanie Hoppe; Jan Winter; Diana Tichy; Christine Zeller; Marius D. Küpper; Eva C. Schitter; Renata Blatnik; Angelika B. Riemer
10.1158/2326-6066.CIR-18-0584
Research Articles
Ines P. Nearchou; Kate Lillard; Christos G. Gavriel; Hideki Ueno; David J. Harrison; Peter D. Caie
10.1158/2326-6066.CIR-18-0377
Research Articles
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
10.1158/2326-6066.CIR-17-0692
Research Articles
Juliane Liepe; John Sidney; Felix K.M. Lorenz; Alessandro Sette; Michele Mishto
10.1158/2326-6066.CIR-18-0424