Academic Commons

Theses Doctoral

Computational Inferences of Mutations Driving Mesenchymal Differentiation in Glioblastoma

Chen, James C.

This dissertation reviews the development and implementation of integrative, systems biology methods designed to parse driver mutations from high- throughput array data derived from human patients. The analysis of vast amounts of genomic and genetic data in the context of complex human genetic diseases such as Glioblastoma is a daunting task. Mutations exist by the hundreds, if not thousands, and only an unknown handful will contribute to the disease in a significant way. The goal of this project was to develop novel computational methods to identify candidate mutations from these data that drive the molecular differentiation of glioblastoma into the mesenchymal subtype, the most aggressive, poorest-prognosis tumors associated with glioblastoma.

Files

  • thumnail for Chen_columbia_0054D_11499.pdf Chen_columbia_0054D_11499.pdf application/pdf 8.7 MB Download File

More About This Work

Academic Units
Genetics and Development
Thesis Advisors
Califano, Andrea
Degree
Ph.D., Columbia University
Published Here
July 16, 2013
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.