Accelerate BASSO Network Center
1.
Minimal Ontology Development Guideline (101). Practical, easy-to-follow guidance for non-ontology experts.
2.
White Paper on Ontology Interoperability. Best-practice recommendations for aligning and integrating ontologies.
3.
Ontology Evaluation Tool. Tools and methods to assess ontology quality and usability.
1.
Develop shared resources that establish a common framework for BSSR ontology development and use across the Network.
2.
Guide the creation and adoption of tools, focusing on creating a common technical framework and SOPs for BSSR ontology development within the U01 Research Network.
3.
Provide technical, computational, and informatics expertise and support for ontology-related activities across the Network.
1.
The White Paper on Ontology Interoperability outlines key challenges, principles, and recommended best practices for achieving effective interoperability among ontologies used across the Network. It provides guidance on aligning and integrating ontologies, promoting reuse of existing standards, and summarizing current approaches and practical strategies for real-world research settings. An initial draft has been developed and is currently being refined, with plans to circulate it for broader review in the coming months.
2.
The Minimal Ontology Development Guideline provides a concise set of recommendations to support researchers—especially those without extensive ontology expertise—in developing and maintaining ontologies within the Network, covering scope definition, ontology design, consistency, interoperability, and documentation. The initial draft has been developed collaboratively, and the first round of internal review has been completed, with feedback currently being incorporated.
3.
The Ontology Evaluation Tool, Hootation, is an ontology verbalization tool that converts logical axioms in OWL into human-readable natural language statements, enabling subject matter experts—including those without deep ontology expertise—to assess whether encoded knowledge accurately reflects intended domain concepts. The current version includes a desktop GUI, and recent updates from Dr. Muhammad Amith’s team have integrated large language model (LLM) capabilities to enhance functionality. The working group is currently testing Hootation, with plans to promote its use across the broader Research Network.
Chair
Cui Tao, PhD, Department of Artificial Intelligence and Informatics, Mayo Clinic, tao.cui@mayo.edu
Cui Tao is the inaugural Enterprise Chair of the Department of Artificial Intelligence and Informatics at Mayo Clinic. She serves as Vice President of Mayo Clinic Platform and the Enterprise Deputy Co-director of Data Science and Artificial Intelligence at the Mayo Clinic Comprehensive Cancer Center. She is a Professor of Biomedical Informatics and an elected fellow of the American College of Medical Informatics.
Her background is in clinical informatics and computer science, and her research interests include ontologies, knowledge graphs, predictive modeling, deep learning, large language models, as well as applying these technologies to clinical and translational studies. She is a recipient of the Presidential Early Career Awards for Scientists and Engineers (PECASE), the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers. Her collective research funding surpasses $30 million. She has a Ph.D. in computer science from Brigham Young University.
Chair
Robert West, PhD, University College London, United Kingdom
Prof West co-founded the Behaviour Change Wheel, the Capability-Opportunity-Motivation-Behaviour (COM-B) model of behaviour, and the PRIME Theory of motivation. He has more than 900 academic publications including numerous books on behavioural science. He is former Editor-in-Chief of the academic journal, Addiction, and has acted as an advisor to the UK Government and currently advises the Welsh Government.
Project Lead
Yue Yu, PhD, Department of Artificial Intelligence and Informatics, Mayo Clinic, yu.yue1@mayo.edu
Yue Yu is an Assistant Professor of Biomedical Informatics and a Senior Data Science Analyst in the Department of Artificial Intelligence and Informatics at Mayo Clinic. Dr. Yu's background is in biomedical informatics, and his research interests include medical data standardization, common data models, and machine learning, as well as the application of artificial intelligence technologies to support clinical and translational research. He earned his Ph.D. in Public Health from Jilin University.