The understanding of human diseases and drug mechanisms requires today to take into account molecular interaction networks. Recent studies on biological systems are producing increasing amounts of data. However, complexity and heterogeneity of these datasets make it difficult to exploit them for understanding atypical phenotypes or drug side-effects. This thesis presents two knowledge-based integrative approaches that combine data management, graph visualization and data mining techniques in order to improve our understanding of phenotypes associated with genetic diseases or drug side-effects. Data management relies on a generic data warehouse, NetworkDB, that integrates data on proteins and their properties. Customization of the NetworkDB model and regular updates are semi-automatic. Graph visualization techniques have been coupled with NetworkDB. This approach has facilitated access to biological network data in order to study genetic disease etiology, including X-linked intellectual disability (XLID). Meaningful sub-networks of genes have thus been identified and characterized. Drug side-effect profiles have been extracted from NetworkDB and subsequently characterized by a relational learning procedure coupled with NetworkDB. The resulting rules indicate which properties of drugs and their targets (including networks) preferentially associate with a particular side-effect profile.
