  Most statistical classifiers are designed to find patterns in data where
numbers fit into rows and columns, like in a spreadsheet, but many kinds of
data do not conform to this structure. To uncover patterns in non-conforming
data, we describe an approach for modifying established statistical classifiers
to handle non-conforming data, which we call dynamic kernel matching (DKM). As
examples of non-conforming data, we consider (i) a dataset of T-cell receptor
(TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR
repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating
that both datasets contain signatures for diagnosing disease. We successfully
fit statistical classifiers augmented with DKM to both datasets and report the
performance on holdout data using standard metrics and metrics allowing for
indeterminant diagnoses. Finally, we identify the patterns used by our
statistical classifiers to generate predictions and show that these patterns
agree with observations from experimental studies.
