Honors Thesis Presentation

Student: John Hocter

Title: Integrative Genomic Analysis for Improved Prediction


Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples.  Each data type provides a snapshot of the molecular processes involved in a particular phenotype.  An integrative -omic analysis, by combining the complementary information from the different data types, can provide a better understanding of the complex biological mechanisms involved in the etiology or progression of a disease. 

In this thesis, I focused on ovarian cancer and used data from The Cancer Genome Atlas (TCGA) project to build predictive models of survival. I investigated various models ranging from using clinical data alone to an integrative model combining clinical and various genomic data (gene expression, methylation, copy number variation). I used variable selection in Cox survival models, and in particular the lasso technique, to identify relevant biomarkers. To evaluate the predictive performance of the models, I used time-dependent ROC curves and concordance index measures based on repeated k-fold cross-validation. I demonstrate  that there is a substantial gain in prediction when integrating the various genomic data sets to the clinical data.

Monday, April 24 at 11:00am to 11:30am

St. Mary's Hall, 326
3700 Reservoir Road, N.W., Washington

Event Type

Academic Events


Georgetown College, Mathematics and Statistics

Event Contact Name

Mahlet Tadesse

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