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Updated: May 18, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
A hierarchical semiparametric model for incorporating intergene information for analysis of genomic data
Long Qu1, Dan Nettleton, Jack C M Dekkers
1BioStat Solutions, Inc., MD 21771, USA.
Abstract:
For analysis of genomic data, e.g., microarray data from gene expression profiling experiments, the two-component mixture model has been widely used in practice to detect differentially expressed genes. However, it naïvely imposes strong exchangeability assumptions across genes and does not make active use of a priori information about intergene relationships that is currently available, e.g., gene annotations through the Gene Ontology (GO) project. We propose a general strategy that first generates a set of covariates that summarizes the intergene information and then extends the two-component mixture model into a hierarchical semiparametric model utilizing the generated covariates through latent nonparametric regression. Simulations and analysis of real microarray data show that our method can outperform the naïve two-component mixture model.
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