Bayesian hierarchical graph-structured model for pathway analysis using gene expression data
نویسندگان
چکیده
منابع مشابه
Bayesian hierarchical graph-structured model for pathway analysis using gene expression data.
In genomic analysis, there is growing interest in network structures that represent biochemistry interactions. Graph structured or constrained inference takes advantage of a known relational structure among variables to introduce smoothness and reduce complexity in modeling, especially for high-dimensional genomic data. There has been a lot of interest in its application in model regularization...
متن کاملBayesian hierarchical error model for analysis of gene expression data
MOTIVATION Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization a...
متن کاملComments on "Bayesian hierarchical error model for analysis of gene expression data"
Cho and Lee (2004) proposed a Bayesian hierarchical error model (HEM) to account for heterogeneous error variability in oligonucleotide microarray experiments. They estimated the parameters of their model using Markov Chain Monte Carlo (MCMC) and proposed an F-like summary statistic to identify differentially expressed genes under multiple conditions. Their HEM is one of the emerging Bayesian h...
متن کاملResponse to comments on "Bayesian Hierarchical Error Model for Analysis of Gene Expression Data"
We greatly thank the authors of this letter for pointing out the significance of our original contribution of the hierarchical error model (HEM) in Cho and Lee (2004). As the authors suggested, we agree that an extension of HEM can be made for gene expression data with biological and/or experimental correlations. However, we here discuss several issues in response to some of the points raised i...
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Housing price data is correlated to their location in different neighborhoods and their correlation is type of spatial (location). The price of housing is varius in different months, so they also have a time correlation. Spatio-temporal models are used to analyze this type of the data. An important purpose of reviewing this type of the data is to fit a suitable model for the spatial-temporal an...
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ژورنال
عنوان ژورنال: Statistical Applications in Genetics and Molecular Biology
سال: 2013
ISSN: 1544-6115,2194-6302
DOI: 10.1515/sagmb-2013-0011