Department of Computer Engineering
CS 590 SEMINAR
Partially Ordered Expression Features Improves Survival Prediction in Cancer
Halil İbrahim Kuru
Computer Engineering Department
Predicting the survival of cancer patients is critical for choosing patient specific treatment strategies. Survival prediction has been traditionally based on clinical or pathological factors such as patient age and tumor stage. With the availability of high-throughput data expression quantities are also incorporated in the models. The survival models that are built with molecular expression profiles rely on the individual expression quantities of the molecules in the tumors. However, in the cell molecules interact with each other and in cancer these interactions are dysregulated in various ways. A better representation of the molecular abundance that accounts for these dysregulations has potential to increase the predictive performance of survival models and help reach biomarkers goes beyond individual molecules. To reach results that are biologically relevant and readily interpretable, we suggest using partial ordering of the expression quantities in lieu of individual expression values. In this work, we focused on protein expression data as it is more stable; however, the same framework is applicable to other molecular types as well. We built random forest survival (RSF) models with partial order features of protein expression data and compare them with the models trained with individual protein expression features in 8 different cancer types. The results demonstrate that partial order features have better predictive performance in the majority of the cancers. Accounting order dysregulation of proteins unveil predictive features with direct relevance to the biological mechanism of cancer. Below, we first describe the methodology and next results obtained.
DATE: 06 November, 2017, Monday @ 15:40