Currently, clinicians practice medicine on a population level. The ability to molecularly characterize biological systems affords new opportunities in the personalization of patient treatment. Proper integration and interpretation of biological data types are necessary to deconvolute individual homeostatic imbalances. Network representation of this information most closely resembles our current models, and the application of machine learning methods to this data structure can help us understand complex interactions. Will is a student in the Pharmaceutical Sciences and Pharmacogenomics graduate degree program.