  A central challenge in every field of biology is to use existing measurements
to predict the outcomes of future experiments. In this work, we consider the
wealth of antibody inhibition data against variants of the influenza virus. Due
to this viru's genetic diversity and evolvability, the variants examined in one
study will often have little-to-no overlap with other studies, making it
difficult to discern common patterns or unify datasets for further analysis. To
that end, we develop a computational framework that predicts how an antibody or
serum would inhibit any variant from any other study. We use this framework to
greatly expand seven influenza datasets utilizing hemagglutination inhibition,
validating our method upon 200,000 existing measurements and predicting
2,000,000 new values along with their uncertainties. With these new values, we
quantify the transferability between seven vaccination and infection studies in
humans and ferrets, show that the serum potency is negatively correlated with
breadth, and present a tool for pandemic preparedness. This data-driven
approach does not require any information beyond each virus's name and
measurements, and even datasets with as few as 5 viruses can be expanded,
making this approach widely applicable. Future influenza studies using
hemagglutination inhibition can directly utilize our curated datasets to
predict newly measured antibody responses against ~80 H3N2 influenza viruses
from 1968-2011, whereas immunological studies utilizing other viruses or a
different assay only need a single partially-overlapping dataset to extend
their work. In essence, this approach enables a shift in perspective when
analyzing data from "what you see is what you get" into "what anyone sees is
what everyone gets."
