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Identifying Gene Clusters and Regulatory Themes using Time Course Expression Data, Hidden Markov Models and Transcription Factor Information

By Karen Lees, Jennifer Taylor, Gerton Lunter and Jotun Hein


Motivation: The development of microarrays has allowed the gene expression of thousands of genes to be simultaneously observed. Methods are required that analyse this expression data in order to discover information about gene function and regulatory mechanisms. The regulatory information would be very useful for the understanding of biological processes and the changes to these processes associated with disease and development. Methods of clustering genes that show similar expression patterns using hidden Markov models have recently been developed for time series data. We extend this previous work by suggesting some improvements as well as developing a strategy to incorporate transcription factor information directly into the model. Results: We applied the method to a large dataset containing 22283 genes and 5 time points. The results showed that expression patterns had been sensibly clustered, however many further improvements and analysis of the method need to be made

Year: 2011
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