Forecasting influenza hemagglutinin mutations through the lens of anomaly detection.

ABSTRACT: 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 significant attention. A historical record of mutations has been used to train predictive models in such solutions. However, the imbalance between mutations and preserved proteins is a big challenge for the development of such models that need 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

SUBMITTER: Garjani A 

PROVIDER: S-EPMC10495359 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature