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Stingo FC, Chen YA, Tadesse MG, et al. INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES. Ann Appl Stat. 2011;5(3):1978-2002doi: 10.1214/11-AOAS463.
Stingo, F. C., Chen, Y. A., Tadesse, M. G., & Vannucci, M. (2011). INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES. The annals of applied statistics, 5(3), 1978-2002. https://doi.org/10.1214/11-AOAS463
Stingo, Francesco C, et al. "INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES." The annals of applied statistics vol. 5,3 (2011): 1978-2002. doi: https://doi.org/10.1214/11-AOAS463
Stingo FC, Chen YA, Tadesse MG, Vannucci M. INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES. Ann Appl Stat. 2011 Sep 01;5(3):1978-2002. doi: 10.1214/11-AOAS463. PMID: 23667412; PMCID: PMC3650864.
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Ann Appl Stat. 2011 Sep 01;5(3):1978-2002. doi: 10.1214/11-AOAS463.

INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES.

The annals of applied statistics

Francesco C Stingo, Yian A Chen, Mahlet G Tadesse, Marina Vannucci

Affiliations

  1. Department of Statistics Rice University Houston, Texas 77005 USA [email protected].

PMID: 23667412 PMCID: PMC3650864 DOI: 10.1214/11-AOAS463

Abstract

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.

Keywords: Bayesian variable selection; Markov chain Monte Carlo; Markov random field prior; gene expression; pathway selection

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