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!

• Automated Analysis of Lymphocytic Infiltration, Tumor Budding, and Their Spatial Relationship Improves Prognostic Accuracy in Colorectal Cancer
  Nearchou IP…Caie PD Cancer Immunology Research April 2019.
• Computational Immune Monitoring Reveals Abnormal Double-Negative T Cells Present across Human Tumor Types
  Greenplate AR…Irish JM Cancer Immunology Research January 2019.
• Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes
  Smith CC…Vincent BG Cancer Immunology Research October 2019.
• Mapping the MHC Class I–Spliced Immunopeptidome of Cancer Cells
  Liepe J…Mishto M Cancer Immunology Research January 2019.
• Performance Evaluation of MHC Class-I Binding Prediction Tools Based on an Experimentally Validated MHC–Peptide Binding Data Set
  Bonsack M…Riemer AB Cancer Immunology Research May 2019.