Towards Integrative Data Analysis for Predictive Modeling in Biomedical Computing

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This research is aimed at developing innovative methods to analyze medical data and understand the mechanisms of actions of complex disease, specifically prostate cancer (PCa). The new era of large-scale biology is characterized by insurmountable amounts of data including medical images, high throughput genetic information, large public databases, and clinical signals. Data abundance stands in contrast to our limited knowledge about the biological events underlying disease. The proposed research seeks to close this gap through a novel framework that uses state-of-the-art image analysis methods, and integrates molecular data and biological information, to formulate more accurate models to characterize cancer. Although PCa is the third leading cancer-related cause of death in North American men, if diagnosed early, it can be managed with a 90% long-term disease free survival rate. Diagnosis of PCa requires biopsy, guided by ultrasound. However, ultrasound guidance is not tailored to an individual and has poor sensitivity in detection of cancer, specifically its aggressive forms. There is an urgent need to clearly distinguish between aggressive PCa that requires intervention and indolent PCa that can be put on “watchful waiting”. The algorithms developed here will address this challenge by i) augmenting ultrasound images with information from probability maps indicating the aggressiveness of cancer, ii) comprehensive analysis of molecular information to stratify disease outcome; and iii) integration of features at multiple resolutions of data to build a comprehensive model of PCa. The unparalleled methods for acquisition and analysis of ultrasound signals provide enhanced prediction of the existence and extent of PCa. The innovative models formulated by integration of features of multiple modalities facilitate the discovery of personalized therapies and biopsies. The methods and software built will help the clinicians make better management decisions for PCa to reduce its mortality while balancing it against the risks of unnecessary overtreatment. The computational methods sought will advance research in image and signal processing, machine learning, systems biology and software engineering. Methods are discussed in the framework of Pca; however, they are extendible to other disorders. Training of highly qualified personnel is a primary focus of this work; three PhD and three Master’s students, as well as five summer undergraduates will be involved in this research program.