Chairperson

Recent advances in high throughput technologies have led to an increasing amount of rich and diverse biological data and related literature. Model organisms are classically selected as subjects for studying human disease based on their genotypic and phenotypic features. A significant problem with model organism identification is the determination of character-istic features related to biological processes that can provide insights into the mechanisms underlying diseases. These insights could have a positive impact on the diagnosis and man-agement of diseases and the development of therapeutic drugs. The increased availability of biological data presents an opportunity to develop data mining methods that can address these challenges and help scientists formulate and test data-driven hypotheses. In this dissertation, data mining methods were developed to provide a quantitative ap-proach for the identification of potential model organisms based on underlying features that may be correlated with disease manifestation in humans. The work encompassed three ma-jor types of contributions that aimed to address challenges related to inferring information from biological data available from a range of sources. First, new statistical models and al