  The influenza virus hemagglutinin is an important part of the virus
attachment to the host cells. The hemagglutinin proteins are one of the genetic
regions of the virus with a high potential for mutations. Due to the importance
of predicting mutations in producing effective and low-cost vaccines, solutions
that attempt to approach this problem have recently gained a significant
attention. A historical record of mutations have been used to train predictive
models in such solutions. However, the imbalance between mutations and the
preserved proteins is a big challenge for the development of such models that
needs to be addressed. Here, we propose to tackle this challenge through
anomaly detection (AD). AD is a well-established field in Machine Learning (ML)
that tries to distinguish unseen anomalies from the normal patterns using only
normal training samples. By considering mutations as the anomalous behavior, we
could benefit existing rich solutions in this field that have emerged recently.
Such methods also fit the problem setup of extreme imbalance between the number
of unmutated vs. mutated training samples. Motivated by this formulation, our
method tries to find a compact representation for unmutated samples while
forcing anomalies to be separated from the normal ones. This helps the model to
learn a shared unique representation between normal training samples as much as
possible, which improves the discernibility and detectability of mutated
samples from the unmutated ones at the test time. We conduct a large number of
experiments on four publicly available datasets, consisting of 3 different
hemagglutinin protein datasets, and one SARS-CoV-2 dataset, and show the
effectiveness of our method through different standard criteria.
