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immuneML

Introduction

immuneML is a machine learning framework for the analysis and classification of adaptive immune receptors and repertoires (AIRR), as initially described in Pavlovic et al. Developed by ELIXIR Norway, immuneML enables researchers to train ML models, apply them to new datasets, simulate benchmarking data, and perform exploratory analyses of immune receptor data.

Key Features

  • Repertoire classification — train models for disease prediction based on immune repertoire data
  • Receptor sequence classification — predict antigen binding and other receptor-level properties
  • Dataset simulation — generate synthetic AIRR datasets for ML model benchmarking
  • Exploratory analysis — statistical analyses, dimensionality reduction, and clustering for deeper insight into immune data
  • Reproducible workflows — YAML-based specification of complete analysis pipelines
  • Broad applications — supports research in immunotherapy, vaccine development, and disease diagnostics

Getting Started

  1. Visit immuneML to access the web interface
  2. Follow the Quickstart tutorial to set up your first analysis
  3. Define your analysis using YAML specifications or use the provided templates
  4. Train, evaluate, and apply models to your AIRR datasets

Links

  • immuneML — web platform and documentation