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Type of Document Master's Thesis Author Statnikov, Alexander R Author's Email Address alexander.statnikov@vanderbilt.edu URN etd-07282005-135526 Title Automatic Cancer Diagnostic Decision Support System for Gene Expression Domain Degree Master of Science Department Biomedical Informatics Advisory Committee
Advisor Name Title Constantin F. Aliferis Committee Chair Douglas P. Hardin Committee Member Ioannis Tsamardinos Committee Member Shawn Levy Committee Member Keywords
- Gene Expression Microarray Analysis
- Decision Support Systems
- Diagnosis
- Neoplasms
- Computer-Assisted
- Artificial Intelligence
Date of Defense 2005-07-06 Availability unrestricted Abstract The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, we have built a system called GEMS (Gene Expression Model Selector) for the automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. In order to determine and equip the system with the best performing diagnostic methodologies in this domain, we first conducted a comprehensive evaluation of classification algorithms using 11 cancer microarray datasets. After the system was built, we performed a preliminary evaluation of the system with 5 new datasets. The performance of the models produced automatically by GEMS is comparable or better than the results obtained by human analysts. Additionally, we performed a cross-dataset evaluation of the system. This involved using a dataset to build a diagnostic model and to estimate its future performance, then applying this model and evaluating its performance on a different dataset. We found that models produced by GEMS indeed perform well in independent samples and, furthermore, the cross-validation performance estimates output by the system approximate well the error obtained by the independent validation. GEMS is freely available for download for non-commercial use from http://www.gems-system.org.Files
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