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.


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More About This Work

Academic Units
Genetics and Development
Thesis Advisors
Califano, Andrea
Ph.D., Columbia University
Published Here
July 16, 2013