Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning
Maria Chernigovskaya,
Milena Pavlović,
Chakravarthi Kanduri
et al.
Abstract:Machine-learning methods (ML) have shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML approaches where experimental data is inaccessible or insufficient as of yet. The challenge for simulated data to be usef… Show more
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