*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Chanchala Kaddi
Ph.D. Defense Presentation
Friday, May 29th 2015, 2pm
Petit Institute Suddath Seminar Room (IBB 1128)
Advisor: May D. Wang, Ph.D. (Georgia Institute of Technology)
Committee:
Melissa Kemp, Ph.D. (Georgia Institute of Technology)
Dong M. Shin, M.D. (Georgia Institute of Technology, Winship Cancer Institute, Emory University)
Brani Vidakovic, Ph.D. (Georgia Institute of Technology)
Howard Weiss, Ph.D. (Georgia Institute of Technology)
Mathematical Models for Data Mining and System Dynamics to Study Head and Neck Cancer Progression and Chemoprevention
Head and neck squamous cell carcinoma (HNSCC) is the 6th most prevalent cancer worldwide, and more than 12,000 deaths from this disease are anticipated in 2015 in the U.S. alone. The advancement of genomic, transcriptomic, and other –omic data acquisition technologies has enabled deeper exploration of the molecular-level mechanisms behind HNSCC development and progression. The insights gained through data-driven analysis can lead to earlier diagnosis and better treatment strategies, and ultimately can result in better patient outcomes. However, the volume and complexity of –omic data remain great challenges.
The goal of this research is to address several key challenges related to applying –omic data for HNSCC research. These are: (1) the lack of modeling tools and systems for discovering biomarkers at the protein and metabolite levels; (2) the lack of effective strategies for integrating heterogeneous types of –omic data for prediction; and (3) the lack of systems-level representations of biomarker knowledge for effectively predicting responses to bioactive agents. Overall, this research accelerates knowledge extraction, enables prediction using –omic data for HNSCC, and delivers a suite of mathematical modeling tools for data mining and system dynamics.
