Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies. (arXiv:2206.14566v2 [q-bio.QM] UPDATED)

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 …

arxiv bio interactions machine machine learning studies virus