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Gene Expression Classification
Author: Gregory M Barrett, CS
Advisors: Carolina Ruiz, Elizabeth Ryder
In this project, we constructed classification models for gene expression based on association rules. Gene expression patterns from cell types in the nematode C. elegans were used. The promoter regions associated with these genes were gathered. Motifs were mined from this data set. Multiple methods were used to select subsets of these motifs. Classification models were built from these subsets. The performance of these models was analyzed. A novel method of selecting motifs was shown to produce the best models.
Cluster Visualization of Upregulated HDAC1 in Mouse Using Integration of Treeview and Galaxy
Author: Timothy Bonci
Advisor: Elizabeth Ryder
In order to better understand genetic expression changes in experiments, microarray data is computer analyzed by tools such as the freely available Galaxy, which currently lacks microarray visualization. A visualization interface was built into the toolset using Python. It was enabled with selectable clustering algorithms which were used to analyze the RNA from mice infected with AAV to upregulate HDAC1 or LacZ. The HDAC enzymes have been shown to play a part in the negative regulation of types of learning. This clustering identifies other genes as likely actors in the chemistry of memory and learning.
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