Translating biomarkers between multi-way time-series experiments

TL;DR

This paper introduces a Bayesian framework for translating biomarkers across multi-species 'omics' experiments with complex, irregularly-sampled multi-way time-series data, addressing limitations of existing methods.

Contribution

It presents a novel Bayesian model capable of inferring unknown variable matchings in complex multi-way, time-series 'omics' experiments across species.

Findings

Successfully infers variable matchings without prior knowledge.

Handles irregularly-sampled, multi-way time-series data.

Extends biomarker translation to complex experimental designs.

Abstract

Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes or metabolites) between the species. However, many experiments have a complicated multi-way experimental design often involving irregularly-sampled time-series measurements, and for instance metabolites do not always have known matchings between organisms. We introduce a Bayesian modelling framework for translating between multiple species the results from 'omics' experiments having a complex multi-way, time-series experimental design. The underlying assumption is that the unknown matching can be inferred from the response of the variables to multiple covariates including time.