Abstract
Data fusion is the process of integrating multiple data sources with the overall goal of producing an accurate, consistent, and useful information than that provided by individual sources. Within the context of predictive toxicology and related applications in the context of chemical risk assessments, a data fusion framework allows integration of various toxicological datasets and endpoints from disparate sources to estimate potential risk of chemicals. Data fusion tools and frameworks have been used in both public and clinical health settings for better health outcomes. This chapter discusses various methodological approaches involved in the data fusion architecture and applications within the context of toxicology and risk assessments.
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