The primary purpose of this project is to develop new metrics and mechanisms for the evaluation of conflicts of interest (COI) risks in the biomedical research enterprise. The innovation in COI risk evaluation and communication will draw on a shift away from the conceptualization of COI as a problem of individual researchers toward an understanding of COI as network phenomena. The pressing problems of COI and the bias it inculcates stem not from individuals but from the aggregation of COI across networks of researchers and funders. The research team will leverage machine-learning and high performance computing to (1) track the circulation of COI within biomedical decision networks, and (2) evaluate the extent to which certain conflict network profiles predict increased risks of patient harm. The team will test candidate COI metrics with U.S. Food and Drug Administration's Adverse Events Reporting System drug safety data. Using these data as a foundation, this project will also (3) develop and evaluate strategies for communicating COI risks of bias designed to safeguard against the perverse outcomes in current disclosure practices. The results of this research will underwrite novel evidence-based recommendations for COI policies in biomedical research as well as recommendations for more effective disclosure practices. To achieve these aims, this project will leverage machine-learning to identify systemic COI networks within the biomedical research enterprises for specific drug products. The research team will then evaluate to what extent network metrics predict relative increases in adverse event rates and severity for identified products.