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2005
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Biological Pathway Prediction from Multiple Data Sources Using Iterative Bayesian Updating

9 years 1 months ago
Biological Pathway Prediction from Multiple Data Sources Using Iterative Bayesian Updating
There is a diversity of functional genomics data, such as gene expression data from microarray experiments, phenotypic data from gene deletion experiments, protein-protein interaction data, and data from manually curated databases of gene function. Each data source finds certain types of relationships between genes and misses other types of relationships. A method that can combine multiple data sources might then be able to uncover more relationships than a method that depends on a single data source. This paper presents a method that uses an iterative Bayesian updating technique to combine data from multiple sources, represented as undirected weighted graphs, in order to estimate the probability that a gene is part of a given biological pathway. This method improves performance over a guilt by association approach for several well characterized biological pathways.
Corey Powell, Joshua M. Stuart
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CSB
Authors Corey Powell, Joshua M. Stuart
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