DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning

High-throughput immunosequencing allows reconstructing the immune repertoire of an individual, which is a unique opportunity for new immunotherapies, immunodiagnostics, and vaccine design. Since immune repertoires are shaped by past and current immune events, such as infection and disease, and thus record an individual9s state of health, immune repertoire sequencing data may enable the prediction of health and disease using machine learning. However, finding the connections between an individual9s repertoire and the individual9s disease class, with potentially hundreds of thousands to millions of short sequences per individual, poses a difficult and unique challenge for machine learning methods. In this work, we present our method DeepRC that combines a Deep Learning architecture with attention-based multiple instance learning. To validate that DeepRC accurately predicts an individual9s disease class based on its immune repertoire and determines the associated class-specific sequence motifs, we applied DeepRC in four large-scale experiments encompassing ground-truth simulated as well as real-world virus infection data. We demonstrate that DeepRC outperforms all tested methods with respect to predictive performance and enables the extraction of those sequence motifs that are connected to a given disease class.