NEW Webcast | Ontologies - the missing foundation for AI in drug discovery
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Webcast supported by
June 17th, 3pm BST
Discover why life sciences, pharma and research organisations are investing in ontologies to improve data quality, interoperability and AI outcomes.
AI initiatives in science and pharma are accelerating rapidly, but many organisations are discovering that fragmented, poorly structured data limits what AI can actually achieve. Increasingly, ontologies and semantic technologies are emerging as the foundation for trustworthy, interoperable and reusable scientific AI workflows.
But what changed? Why are organizations that once avoided ontological approaches now scrambling to implement them? And how can companies navigate the transition from legacy data chaos to structured, AI-ready knowledge systems?
This expert panel brings together three pioneering women who've been championing applied ontologies long before they became fashionable. From software innovation to pharmaceutical implementation to consulting expertise, they'll explore the practical realities of ontology adoption in modern scientific organizations.
What we'll cover:
Foundations for Scientific AIWhy ontologies are becoming critical infrastructure for AI and scientific data strategies |
Enabling Data InteroperabilityHow pharma and life science organisations use semantic frameworks to improve data interoperability |
Connecting Scientific PlatformsWhere ontologies fit within ELNs, LIMS and scientific informatics platforms |
FAIR Data StrategiesThe relationship between FAIR data, knowledge graphs and AI readiness |
Ontology Myths DebunkedCommon misconceptions and barriers to ontology adoption |
Launching Semantic StrategiesPractical first steps for organisations exploring ontology-driven AI initiatives |
Calling all:
📊Scientific data and informatics leaders | 🖥️AI and machine learning teams working with scientific data |
🧪ELN/LIMS and laboratory platform managers | 📚FAIR data and knowledge management specialists |
👩🔬Bioinformatics and cheminformatics professionals | 🤖Research organisations exploring knowledge graphs and semantic AI |
👩⚖️R&D IT architects and digital transformation leaders | 👩🏫Academic researchers transitioning to industry |
Speakers
Samantha Jeschonek, PhD. Samantha Jeschonek is the product manager for aata-driven Analytics in Signals One. She holds a Ph.D. in Molecular Biology, Cell Biology, and Biochemistry. Samantha has spent the last several years as a product manager for drug discovery informatics software. In addition to more than a decade of bench experience in molecular biology, she brings a passion for FAIR (meta)data and ontologies to the Revvity Signals product team. |  |
Dr. Jane Lomax Dr. Jane Lomax leads a team providing ontology and terminology expertise to help clients unlock the full value of their data. She has more than 20 years of experience in ontologies and FAIR data, with previous roles at SciBite/Elsevier, EMBL-EBI, and the Wellcome Sanger Institute. Jane earned her PhD in Genetics from the University of Cambridge and has authored over 50 publications on biomedical ontology development and applications. She has contributed extensively to community efforts, including the OBO Foundry, the International Society of Biocuration, and ELIXIR, and previously served on the board of the Pistoia Alliance. |  |
Julia L. Fox, PhD. Julia Fox has led semantically driven Data & Digital Transformation, spearheading adoption of digitised Clinical Data Standards, domain ontologies and embedded semantic models in pharma settings. She had led multiple efforts to broadly define and support metadata-driven approaches in clinical trial & data management; next-gen lab solution implementation and adoption; and cohesive master data and data platform integration. Julia drives cross-functional stakeholder alignment via close collaboration with clinical sciences, therapeutic areas, IT and business units across pharma organisations. She has a strong scientific background in developmental genetics, genomics and drug discovery informatics with a focus on scientific semantics, data curation and cohesive annotation. She will share approaches to developing aligned common data models for institutionally shared metadata object definitions supported by scientific and clinical ontologies. Harmonised metadata and richly annotated data sets accelerate analysis and innovation, drive insights and deliver AI-ready FAIR data. |  |
More speakers to be announced
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