SPH Biostatistics Digital Archive

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  • Item
    PleioGRiP: Pleiotropic Genetic Risk Prediction via Bayesian model search and classification
    (2012-11-16) Sebastiani, Paola; Hartley, Stephen
    The program PleioGRiP performs a genome-wide Bayesian model search to identify SNPs associated with a discrete phenotype, and uses SNPs ranked by Bayes factor to produce nested Bayesian classifiers. These classifiers can be used for genetic risk prediction, either selecting the classifier with optimal number of features, or using an ensemble of classifiers. In addition, PleioGRiP implements an extension to the Bayesian search and classification, and can search for pleiotropic relationships in which SNPs are simultaneously associated with two or more distinct phenotypes. These relationships can be used to generate connected Bayesian classifiers to predict the phenotype of interest either using genetic data alone, or in combination with the secondary phenotype(s).
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    CAGED (Cluster Analysis of Gene Expression Dynamics)
    (2010-01-27T20:28:22Z) Sebastiani, Paola
    Computer program for the analysis of temporal expression profiles of gene expression data
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    BADGE (Bayesian Analysis of Differential Gene Expression)
    (2010-01-27T20:26:13Z) Sebastiani, Paola
    A program for analysis of differential gene expression data using Bayesian model averaging.
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    Bayesware Discoverer
    (2010-01-27T20:21:30Z) Sebastiani, Paola
    A program for learning Bayesian networks from data and reasoning using probabilistic inference.