"Using anti-profiles for anomaly classification" by Héctor Corrada Bravo

Fri Nov 09, 2012 1:00 PM

Location: 2117 Computer Science Instructional Center (CSI)

Speaker: Dr. Héctor Corrada Bravo

Gene expression anti-profiles are a new computational approach for developing cancer genomic signatures that specifically take advantage of gene expression heterogeneity. This presentation will describe the biological basis for this method based on recent experimental findings. Application of this methodology in screening patients for colon cancer based on expression measurements obtained from peripheral blood samples will be presented. We will also present results from development of a universal cancer anti-profile that accurately distinguishes cancer from normal regardless of tissue type. These results suggest that anti-profiles may be used to develop inexpensive and non-invasive universal cancer screening tests. We also introduce the anti-profile Support Vector Machine (apSVM) as a novel algorithm to address the anomaly classification problem, an extension of anomaly detection where the goal is to distinguish data samples from a number of anomalous and heterogeneous classes based on their pattern of deviation from a normal stable class. We show by simulation and application to cancer genomics datasets that the anti-profile SVM produces classifiers that are more accurate and stable than the standard SVM in the anomaly classification setting.