Tom Plasterer
Bioinformatics, AstraZeneca
Tom Plasterer, PhD, Senior Director, Bioinformatics, BioPharmaceutical R&D, AstraZeneca—Dr. Plasterer has pursued interests in bioinformatics, clinical informatics, systems biology and biomarker discovery over the last twenty years in both industry and academia. He co-founded PanGenX (a Personalized Medicine/Pharmacogenetics Knowledge Base start-up), directed the project planning and data interpretation group at BG Medicine, and held an adjunct professor position in the department of Chemistry and Chemical Biology at Northeastern University. He now leads translational science data integration and FAIR data initiatives at AstraZeneca. In these roles at AstraZeneca, Dr. Plasterer has been responsible for establishing and executing the FAIR data strategy which includes knowledge representation, vocabulary & metadata services, semantic visualization, analytics and business case development. This strategy has been deployed to build the competitive intelligence and integrative informatics frameworks for R&D. Tom also serves on the Pistoia Alliance FAIR data advisory board.

2020 Talk: Middle-Out FAIR Data Integration with Knowledge Graphs

An extremely powerful and efficient use for Knowledge Graphs is to unite well-understood domains of knowledge alongside novel and specific business/scientific questions. Using small ontologies that embed large reference taxonomies, we are able to go from tactical scientific questions (bottom-up) to common taxonomies and reference datasets (middle-out) approach to model scientific questions, aligning with enterprise master data strategies on the way up. If building blocks follow FAIR (Findable, Accessible, Interoperable, Reusable) data principles, reusing these processes becomes more and more efficient over time as the middle layer grows. Examples in the translational medicine space will be highlighted.

2019 Talk: FAIR Data Knowledge Graphs From Theory to Practice

  FAIR data has flown up the hype curve without a clear sense of return from the required data stewardship investment. The killer use case for FAIR data is a science knowledge graph. It enables you to richly address novel questions of your and the world's data. We started with data catalogs (findability) which exploited linked/referenced data using a few focused vocabularies (interoperability), for credentialed users (accessibility), with provenance and attribution (reusability) to make this happen. Our processes enable the simple creation of dataset records and linking to source data, providing a seamless federated knowledge graph for novice and advanced users alike.