Difference between revisions of "Discovering Significant Pathways of Gene Regulation"
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| − | Mentor: Serdar Bozdag | + | '''Mentor:''' Serdar Bozdag |
| − | Recent advancements in biotechnology have made it possible to generate vast amounts of gene expression data for thousands of organisms. The collection of high-throughput gene expression data allows computational biologists to develop algorithms to reverse engineer the underlying gene regulatory network (GRN) of cells from their gene expression data. Among several software tools to reverse engineer GRNs, FastMEDUSA is a powerful tool. FastMEDUSA builds a model represented by an alternating decision tree (ADT) that predicts the potential regulators of genes. | + | '''Summary:''' Recent advancements in biotechnology have made it possible to generate vast amounts of gene expression data for thousands of organisms. The collection of high-throughput gene expression data allows computational biologists to develop algorithms to reverse engineer the underlying gene regulatory network (GRN) of cells from their gene expression data. Among several software tools to reverse engineer GRNs, FastMEDUSA is a powerful tool. FastMEDUSA builds a model represented by an alternating decision tree (ADT) that predicts the potential regulators of genes. |
We hypothesize that if there are significantly overrepresented branches in the ADT of FastMEDUSA, they could be biologically important pathways for gene regulation. In this project, we analyze the ADT built by FastMEDUSA to compute significantly overrepresented branches. We compute p-value for each branch based on results on randomly generated ADTs. For validation, we check public databases and literature to verify if genes in these branches have been reported as pathways of gene regulation. | We hypothesize that if there are significantly overrepresented branches in the ADT of FastMEDUSA, they could be biologically important pathways for gene regulation. In this project, we analyze the ADT built by FastMEDUSA to compute significantly overrepresented branches. We compute p-value for each branch based on results on randomly generated ADTs. For validation, we check public databases and literature to verify if genes in these branches have been reported as pathways of gene regulation. | ||
Students will have the opportunity to work on graph theory, statistics, and biological databases to answer some high-impact biological questions. | Students will have the opportunity to work on graph theory, statistics, and biological databases to answer some high-impact biological questions. | ||
Revision as of 02:39, 20 January 2017
Mentor: Serdar Bozdag
Summary: Recent advancements in biotechnology have made it possible to generate vast amounts of gene expression data for thousands of organisms. The collection of high-throughput gene expression data allows computational biologists to develop algorithms to reverse engineer the underlying gene regulatory network (GRN) of cells from their gene expression data. Among several software tools to reverse engineer GRNs, FastMEDUSA is a powerful tool. FastMEDUSA builds a model represented by an alternating decision tree (ADT) that predicts the potential regulators of genes.
We hypothesize that if there are significantly overrepresented branches in the ADT of FastMEDUSA, they could be biologically important pathways for gene regulation. In this project, we analyze the ADT built by FastMEDUSA to compute significantly overrepresented branches. We compute p-value for each branch based on results on randomly generated ADTs. For validation, we check public databases and literature to verify if genes in these branches have been reported as pathways of gene regulation.
Students will have the opportunity to work on graph theory, statistics, and biological databases to answer some high-impact biological questions.
