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Immune repertoire classification, a typical multiple instance learning (MIL)problem, is a frontier research topic in computational biology that makestransformative contributions to new vaccines and immune therapies. However, thetraditional instance-space MIL, directly assigning bag-level labels toinstances, suffers from the massive amount of noisy labels and extremely lowwitness rate. In this work, we propose a noisy-label-learning formulation tosolve the immune repertoire classification task. To remedy the inaccuratesupervision of repertoire-level labels for a sequence-level classifier, wedesign a robust training strategy: The initial labels are smoothed to beasymmetric and are progressively corrected using the model's predictionsthroughout the training process. Furthermore, two models with the samearchitecture but different parameter initialization are co-trainedsimultaneously to remedy the known "confirmation bias" problem in theself-training-like schema. As a result, we obtain accurate sequence-levelclassification and, subsequently, repertoire-level classification. Experimentson the Cytomegalovirus (CMV) and Cancer datasets demonstrate our method'seffectiveness and superior performance on sequence-level and repertoire-leveltasks.

A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification