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 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| 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 |
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 |
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Keywords
- Bayesian Theory
- Bipolar Disorder
- External Cross-Validation
- Gene Expression
ASJC Scopus subject areas
- Biomedical Engineering
- Modelling and Simulation
Cite this
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Bayesian prognostic model for genomic discovery in bipolar disorder
AU - Bobba, Swetha S.
AU - Zollanvari, Amin
AU - Alterovitz, Gil
PY - 2014
Y1 - 2014
N2 - 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.
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.
KW - Bayesian Theory
KW - Bipolar Disorder
KW - External Cross-Validation
KW - Gene Expression
UR - http://www.scopus.com/inward/record.url?scp=84902308433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902308433&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9789897580123
SP - 91
EP - 98
BT - 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
PB - SciTePress
ER -