2022/07/26 by Tal Einav, Yuehaw Khoo, Einav, Tal +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · Medicine · #Biological Physics (physics.bio-ph) #FOS: Biological sciences #FOS: Physical sciences #Monoclonal and Polyclonal Antibodies Research #Physics and Society (physics.soc-ph) #Quantitative Methods (q-bio.QM) #T-cell and B-cell Immunology #vaccines and immunoinformatics approaches
paper · pdf · doi:10.48550/arxiv.2207.12658
openalex publication_date 2022/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily-interpretable form. Often, each measurement only conveys the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are only available for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially-observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior.