Using interpretable machine learning to extend heterogeneous antibody-virus datasets

Tal Einav; Rong Ma

doi:10.1016/j.crmeth.2023.100540

Using interpretable machine learning to extend heterogeneous antibody-virus datasets

Cell Rep Methods. 2023 Jul 25;3(8):100540. doi: 10.1016/j.crmeth.2023.100540. eCollection 2023 Aug 28.

Authors

Tal Einav^{1

2}, Rong Ma³

Affiliations

¹ Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
² Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, USA.
³ Department of Statistics, Stanford University, Stanford, CA 94305, USA.

Abstract

A central challenge in biology is to use existing measurements to predict the outcomes of future experiments. For the rapidly evolving influenza virus, variants examined in one study will often have little to no overlap with other studies, making it difficult to discern patterns or unify datasets. We develop a computational framework that predicts how an antibody or serum would inhibit any variant from any other study. We validate this method using hemagglutination inhibition data from seven studies and predict 2,000,000 new values ± uncertainties. Our analysis quantifies the transferability between vaccination and infection studies in humans and ferrets, shows that serum potency is negatively correlated with breadth, and provides a tool for pandemic preparedness. 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."

Keywords: antibody-virus interactions; error estimation; hemagglutination inhibition; imputation; influenza; matrix completion; serology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Antibodies
Ferrets*
Hemagglutination Inhibition Tests
Humans
Machine Learning
Oils, Volatile*

Substances

Antibodies
Oils, Volatile