Research Projects and Collaborations

Current

Transfer Rule Learning for Knowledge Based Biomarker Discovery and Predictive Biomedicine

This project will develop highly-needed computational methods for integrative biomarker discovery from related but separate data sets produced by predictive molecular profiling studies of disease. It will generate new experimental data for early detection of lung cancer, and has the potential to help create new diagnostic screening tools for lung cancer, a leading cause of death from cancer in the United States.

Bayesian Rule Learning Methods for Disease Prediction and Biomarker Discovery

This project will develop highly-needed data mining methods for analyzing the spate of datasets arising from high-throughput technologies for molecular biomarker profiling. It will generate new experimental data for early detection of breast cancer, and has the potential to help create new diagnostic screening tools for three diverse diseases: two of the most common cancers in the world - lung and breast cancers, and rare, neurodegenerative Amyotrophic Lateral Sclerosis.

SPORE in Lung Cancer (Co-Director of Bioinformatics and Biostatistics CORE)

The objectives of the UPCI Lung Cancer SPORE are to improve detection and treatment of lung cancer and to understand the mechanisms of increased susceptibility of women to lung cancer in collaboration with Dr. Jill Siegfried and Dr. Bill Bigbee.

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