Can the natural system of viruses reconcile the current taxonomy with an alternative classification useful to clinicians?

TL;DR

This paper explores an alternative virus classification system using Neural Replicator Analysis to better align taxonomy with clinical and practical needs, addressing limitations of traditional hierarchical methods.

Contribution

It introduces a neural replicator-based classification approach that refines virus taxonomy, making it more useful for clinicians and other specialists.

Findings

Neural Replicator Analysis can resolve classification contradictions.

The new system better aligns with clinical distinctions.

Preprocessing viral genomes enhances classification accuracy.

Abstract

In 2022, a group of basic and clinical virologists, bioinformaticians, and evolutionary and structural biologists met in Oxford, UK, to develop a consensus on methodologies used to classify viruses. They concluded that virus taxonomy, which is hierarchical and based on evolution, is only one of many possible ways to classify viruses. This taxonomy, while satisfying the four principles they set out, faces difficulties in coordinating with other classification systems useful to clinicians, infectious disease specialists, agronomists, etc. One example discussed is the grouping of different viral strains that cause different diseases into the species Enterovirus C. Here we show that the use of a previously proposed variant of a natural virus classification system based on the use of Neural Replicator Analysis can resolve this contradiction by establishing the fine structure of the Enterovirus C species, in which strains that cause different diseases are placed in several different cells of the binomial table of viruses. A key element in enabling this is the sophisticated preprocessing of the original viral genomes using neural replicators.