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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
Free
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
Research Articles
Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an Experimentally Validated MHC–Peptide Binding Data Set
Available to Purchase
Maria Bonsack; Stephanie Hoppe; Jan Winter; Diana Tichy; Christine Zeller; Marius D. Küpper; Eva C. Schitter; Renata Blatnik; Angelika B. Riemer
Research Articles
Automated Analysis of Lymphocytic Infiltration, Tumor Budding, and Their Spatial Relationship Improves Prognostic Accuracy in Colorectal Cancer
Available to Purchase
Ines P. Nearchou; Kate Lillard; Christos G. Gavriel; Hideki Ueno; David J. Harrison; Peter D. Caie
Research Articles
Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types
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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
Research Articles
Mapping the MHC Class I–Spliced Immunopeptidome of Cancer Cells
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Juliane Liepe; John Sidney; Felix K.M. Lorenz; Alessandro Sette; Michele Mishto