Bayesian prognostic model for genomic discovery in bipolar disorder

Swetha S. Bobba; Amin Zollanvari; Gil Alterovitz

doi:10.5220/0004642100910098

Bayesian prognostic model for genomic discovery in bipolar disorder

Swetha S. Bobba, Amin Zollanvari, Gil Alterovitz

Department of Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 singlystructured 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 crossvalidation. 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. Enrichment analysis performed in this study revealed biological concepts and proteins responsible for the disease. 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 language	English
Title of host publication	BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
Publisher	SciTePress
Pages	91-98
Number of pages	8
ISBN (Print)	9789897580123
DOIs	https://doi.org/10.5220/0004642100910098
Publication status	Published - Jan 1 2014
Event	5th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014 - Angers, Loire Valley, France Duration: Mar 3 2014 → Mar 6 2014

Publication series

Name	BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014

Other

Other	5th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
Country	France
City	Angers, Loire Valley
Period	3/3/14 → 3/6/14

Keywords

Bayesian Theory
Bipolar Disorder
External Cross-Validation
Gene Expression

ASJC Scopus subject areas

Biomedical Engineering
Modelling and Simulation

Access to Document

10.5220/0004642100910098

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Bobba, S. S., Zollanvari, A., & Alterovitz, G. (2014). Bayesian prognostic model for genomic discovery in bipolar disorder. In BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014 (pp. 91-98). (BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014). SciTePress. https://doi.org/10.5220/0004642100910098

Bayesian prognostic model for genomic discovery in bipolar disorder. / Bobba, Swetha S.; Zollanvari, Amin; Alterovitz, Gil.

BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. SciTePress, 2014. p. 91-98 (BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bobba, SS, Zollanvari, A & Alterovitz, G 2014, Bayesian prognostic model for genomic discovery in bipolar disorder. in BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014, SciTePress, pp. 91-98, 5th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2014 - Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014, Angers, Loire Valley, France, 3/3/14. https://doi.org/10.5220/0004642100910098

Bobba SS, Zollanvari A, Alterovitz G. Bayesian prognostic model for genomic discovery in bipolar disorder. In BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. SciTePress. 2014. p. 91-98. (BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014). https://doi.org/10.5220/0004642100910098

Bobba, Swetha S. ; Zollanvari, Amin ; Alterovitz, Gil. / Bayesian prognostic model for genomic discovery in bipolar disorder. BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. SciTePress, 2014. pp. 91-98 (BIOINFORMATICS 2014 - 5th Int. Conf. on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014).

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AB - 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 singlystructured 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 crossvalidation. 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. Enrichment analysis performed in this study revealed biological concepts and proteins responsible for the disease. 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.

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