2010/12/15 by Ilkka Huopaniemi, Tommi Suvitaival, Huopaniemi, Ilkka +5
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Genetic Mapping and Diversity in Plants and Animals #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1012.3407
openalex publication_date 2010/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.