Formal Methods for the Analysis of Biomedical Ontologies - Rilegato

Zhang, Guo-Qiang; Abeysinghe, Rashmie; Cui, Licong

Rilegato

ISBN 10: 3031937759 ISBN 13: 9783031937750

Casa editrice: Springer-Nature New York Inc, 2025

Riassunto

This book explores the application of formal methods, rooted in mathematics and logic, to the analysis and enhancement of biomedical ontologies.  The authors take a pragmatic approach focused on generating actionable insights to achieve high-quality codified biomedical knowledge in the most active and impactful areas where ontologies have a direct real-world impact. The book first introduces simple, yet formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The authors then discuss formal concept analysis as an ontological engineering principle. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies. This Second Edition includes a new chapter that covers recent research on leveraging logical definitions for identifying ontological defects. The authors have also added a new chapter on the perspective of using large language models in the ontological analysis work.

Su questo libro

Informazioni sull?autore

Guo-Qiang "GQ" Zhang is distinguished chair in digital innovation, vice president, and chief data scientist at the University of Texas Health Science Center at Houston (UTHealth Houston). He serves as co-director for the Texas Institute for Restorative Neurotechnologies and holds primary appointment as full professor in the Department of Neurology at UTHealth Houston's McGovern Medical School.  He has secondary joint appointments in the McWilliams School of Biomedical Informatics and School of Public Health, UTHealth Houston.  Before joining UTHealth, he served as the inaugural director for the Institute for Biomedical Informatics, chief of the Division of Biomedical Informatics, and associate director for the Center for Clinical and Translational Science (CTSA) at the University of Kentucky.  He spent prior years as a faculty member at the Case School of Engineering and School of Medicine, at Case Western Reserve University, where he created its Division of Biomedical Informatics in the School of Medicine while serving as its CTSA informatics core director.  Dr. Zhang received his Ph.D.  in Computer Science from Cambridge University. His research work and interests span data ecosystems and big data, biomedical ontology development and quality assurance, clinical and research informatics, and data coordination systems for prospective metadata management in national consortia, such as the latest, on-going BRAIN Initiative Cell Atlas Network (BICAN). He has launched a research program called ``logic-based phenotyping,'' a formalized mathematical approach for temporal phenotype representation and reasoning using an expressive and purpose-fitting framework called "temporal ensemble logic".  His work has been funded by research awards from the National Institutes of Health (NIH) and the National Science Foundation (NSF).

Rashmie Abeysinghe received his B.S. degree in Computer Science from University of Peradeniya, Peradeniya, Sri Lanka in 2014 and Ph.D. degree in Computer Science from University of Kentucky, Lexington, KY, USA in 2020. He completed a Summer Internship at the National Library of Medicine, NIH in 2019. In 2020, he joined the Department of Neurology, McGovern Medical School at the UTHealth Houston, first as a Research Scientist and then as Assistant Professor. His research interests revolves around biomedical ontologies from a quality assurance perspective, information extraction, and deep learning. His paper won a Distinguished Paper Award at the 2021 AMIA Annual Symposium. His papers were also selected as finalists for both the 2018 and 2019 AMIA Annual Symposium Best Student Paper Competitions.

Licong Cui received her Ph.D. in Computer Science from Case Western Reserve University (2014). She is associate professor in McWilliams School of Biomedical Informatics at UTHealth Houston. Before joining UTHealth Houston, she was an assistant professor in the Department of Computer Science and a member of the Institute for Biomedical Informatics at the University of Kentucky. Her research interests include ontologies and terminologies, neuroinformatics, big data analytics, large-scale data integration and management, and information extraction and retrieval. She has been a Principal Investigator of a number of highly competitive research awards funded by the NIH and the NSF. She is a recipient of the prestigious NSF CAREER Award.

Dalla quarta di copertina

This book explores the application of formal methods, rooted in mathematics and logic, to the analysis and enhancement of biomedical ontologies.  The authors take a pragmatic approach focused on generating actionable insights to achieve high-quality codified biomedical knowledge in the most active and impactful areas where ontologies have a direct real-world impact. The book first introduces simple, yet formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The authors then discuss formal concept analysis as an ontological engineering principle. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies. This Second Edition includes a new chapter that covers recent research on leveraging logical definitions for identifying ontological defects. The authors have also added a new chapter on the perspective of using large language models in the ontological analysis work.

In addition, this book:

• Provides actionable insights for improving the quality of biomedical ontologies in research applications
• Covers a broad range of topics in a clear and accessible style, making it appropriate for a wide range of readers
• Presents useful tools, rigorous algorithms, and visualized real-world examples for ontological engineering applications

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031937759
• ISBN 13: 9783031937750
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 272
• Contatto del produttore: non disponibile
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