Jeffrey A. Thompson
ABSTRACT
Gene expression profiles quantify the expression of thousands of genes simultaneously, providing a snapshot in time of gene expression in a specific tissue. A gene expression profile can be helpful in understanding the association of genes to the progression of cancer and patient outcomes. However, these complex associations can be difficult to determine using traditional approaches. In this project, we describe an automated pipeline for clustering patients based on differential gene expression that performs survival analysis and identifies genes that are associated with increased survival time. This method greatly reduces the effort required to perform a relatively complex analysis.
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Index Terms
- An automated pipeline for discovering gene expression patterns associated with increased cancer survival time
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