Integrating Multi-Layered Data for Biomarker Discovery

• Activity Number: 156
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: WNAR
• Author(s): Jean Yee Hwa Yang*+ and Kaushala Jayawardana and Samuel Mueller
• Companies: University of Sydney and University of Sydney and University of Sydney
• Keywords: data integration ; bioinformatics ; gene expression ; omics data

Abstract

Over the last decade, several statistical techniques have been proposed to tackle genome-wide expression data. However, with the advancement of many other high-throughput biotechnologies, the interest of researchers has been focusing on utilizing multiple data sources together with the clinical data, to improve the prognosis of disease outcome.

Integrating the components from different platforms has become a crucial step to better understand the relationships between clinical and -omics data and the information they provide to explain/predict some response. The statistical task to preserve the stability and interpretability of the model has become more challenging in this framework. One major issue is that the large dimension of -omics data can completely dominate the modelling procedure and it is an open question how to best combine different types of variables.

This talk will outline, the integration of clinical and other high-throughput data to improve the prognosis capabilities and determination of biomarkers in stage III melanoma. This is based on a framework, which combines bootstrap sampling and multiple imputation to produce a model with good predictive properties.