Translating biomarkers between multi-way time-series experiments
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.
Cite
@article{arxiv.1012.3407,
title = {Translating biomarkers between multi-way time-series experiments},
author = {Ilkka Huopaniemi and Tommi Suvitaival and Matej Orešič and Samuel Kaski},
journal= {arXiv preprint arXiv:1012.3407},
year = {2010}
}