Graph databases, such as Neo4j, are well-suited for capturing and modeling complex relations in life sciences, particularly in entangled problems like heart failure. The HOMAGE consortium is using graph databases to organize patient information in context of biomedical knowledge, helping clinical researchers grasp the mechanisms driving heart failure and ultimately leading to improved patient care. Integrating data and knowledge involves modeling an incomplete and ever-changing model of how our bodies work and what we know about it, which poses practical and conceptual challenges due to ambiguous labels and redundant ontologies. Graph databases help address these challenges by providing a flexible and agile data model that can anticipate new insights and needs of researchers, storing information in a format that facilitates querying relations and paths. The Neo4j database used in the HOMAGE platform contains over 130 thousand nodes and over 6.5 million relationships, allowing researchers to query and mine relevant information effectively, build predictive models, and identify patterns and key players that may lead to new biomarkers or drug targets. The platform is gaining attention from clinical researchers as a direct way to systematically and quickly query their favorite heart failure biomarkers against existing knowledge, demonstrating the importance of interdisciplinary collaboration between clinicians and data scientists in datafied healthcare.