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Authors: Swetha S. Bobba 1 ; Amin Zollanvari 2 and Gil Alterovitz 3

Affiliations: 1 Vignana Bharathi Institute of Technology, Harvard Medical School and Children’s Hospital Informatics Program at Harvard-MIT, India ; 2 Harvard Medical School, Children’s Hospital Informatics Program at Harvard-MIT and Partners Healthcare Center for Personalized Genetic Medicine, United States ; 3 Harvard Medical School, Children’s Hospital Informatics Program at Harvard-MIT, Partners Healthcare Center for Personalized Genetic Medicine and MIT, United States

ISBN: 978-989-758-012-3

Keyword(s): Bayesian Theory, Gene Expression, Bipolar Disorder, External Cross-Validation.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning ; Genomics and Proteomics

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 r esponsible 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. (More)

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Paper citation in several formats:
Bobba, S.; Zollanvari, A. and Alterovitz, G. (2014). Bayesian Prognostic Model for Genomic Discovery in Bipolar Disorder.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 91-98. DOI: 10.5220/0004642100910098

@conference{bioinformatics14,
author={Swetha S. Bobba. and Amin Zollanvari. and Gil Alterovitz.},
title={Bayesian Prognostic Model for Genomic Discovery in Bipolar Disorder},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004642100910098},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Bayesian Prognostic Model for Genomic Discovery in Bipolar Disorder
SN - 978-989-758-012-3
AU - Bobba, S.
AU - Zollanvari, A.
AU - Alterovitz, G.
PY - 2014
SP - 91
EP - 98
DO - 10.5220/0004642100910098

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