An Automated Bayesian Framework for Integrative Gene Expression Analysis and Predictive Medicine

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An Automated Bayesian Framework for Integrative Gene Expression Analysis and Predictive Medicine

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Title: An Automated Bayesian Framework for Integrative Gene Expression Analysis and Predictive Medicine
Author: Parikh, Neena; Zollanvari, Amin; Alterovitz, Gil

Note: Order does not necessarily reflect citation order of authors.

Citation: Parikh, Neena, Amin Zollanvari, and Gil Alterovitz. 2012. An automated Bayesian framework for integrative gene expression analysis and predictive medicine. AMIA Summits on Translational Science Proceedings 2012: 95-104.
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Abstract: Motivation: This work constructs a closed loop Bayesian Network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases. Results: An automated pipeline was successfully constructed. Integrative models were made based on gene expression data obtained from GEO experiments relating to four different diseases using Bayesian statistical methods. Many of these models demonstrated a high level of accuracy and predictive ability. The approach described in this paper can be applied to any complex disorder and can include any number and type of genome-scale studies.
Published Version: http://proceedings.amia.org/2350ff/1
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392067/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10456094
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