Multimodal probabilistic generative models for time-course gene expression data and Gene Ontology (GO) tags

Prasad Gabbur, James Hoying, Jacobus J Barnard

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

We propose four probabilistic generative models for simultaneously modeling gene expression levels and Gene Ontology (GO) tags. Unlike previous approaches for using GO tags, the joint modeling framework allows the two sources of information to complement and reinforce each other. We fit our models to three time-course datasets collected to study biological processes, specifically blood vessel growth (angiogenesis) and mitotic cell cycles. The proposed models result in a joint clustering of genes and GO annotations. Different models group genes based on GO tags and their behavior over the entire time-course, within biological stages, or even individual time points. We show how such models can be used for biological stage boundary estimation de novo. We also evaluate our models on biological stage prediction accuracy of held out samples. Our results suggest that the models usually perform better when GO tag information is included.

Original languageEnglish (US)
Pages (from-to)80-91
Number of pages12
JournalMathematical Biosciences
Volume268
DOIs
StatePublished - Oct 1 2015

Keywords

  • Cluster
  • Gene expression
  • Microarray
  • Model
  • Ontology

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modeling and Simulation
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

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