MaxSnippetModel

MaxSnippetModel is a software tool that uses machine learning to diagnose autoimmune diseases such as relapsing remitting multiple sclerosis (RRMS) by analyzing the clonal composition of lymphocyte populations. It takes into account the vast amounts of information in the millions of individual immune receptors comprising a repertoire and uses the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102). This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert.