Bayesian prognostic model for genomic discovery in bipolar disorder

Swetha S. Bobba, Amin Zollanvari, Gil Alterovitz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Integrative approaches that incorporate multiple experiments have shown a potential application in the discovery of disease-related attributes. This study presents a unique, data-driven, integrative, Bayesian approach to merge gene expression data from various experiments into prognostic models and evaluate them for the discovery of bipolar-related attributes. Two prognostic models were constructed: a singly-structured Bayesian and a Bayesian multi-net model, which differentiated bipolar disease state at a higher level of abstraction. These prognostic models were evaluated to find the most common attributes responsible for the disease and their AUROC, using external cross-validation. The multi-net model achieved an AUROC of 0.907 significantly outperforming the single- structured model with an AUROC of 0.631. The study found six new genes and five chromosomal regions associated with the bipolar state. We anticipate this method and results will be used in the future to integrate information from multiple experiments for the same or related phenotypes of various diseases and also to predict the disease state earlier.

Original languageEnglish
Title of host publication2014 IEEE Healthcare Innovation Conference, HIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781467363648
Publication statusPublished - Feb 10 2014
Event2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States
Duration: Oct 8 2014Oct 10 2014

Publication series

Name2014 IEEE Healthcare Innovation Conference, HIC 2014


Other2014 IEEE Healthcare Innovation Conference, HIC 2014
CountryUnited States

ASJC Scopus subject areas

  • Medicine(all)
  • Biomedical Engineering

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