
Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care
Description
Key Features
- Provides an analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models
- Outlines the vital role that data modeling/curation has played in significant medical breakthroughs
- Presents readers with an overview of how information-management approaches intersect with precision medicine, providing case studies of data-modeling in concrete scientific practice
- Explores applications of image analysis and computer vision in the context of precision medicine
- Examines the role of technology in scientific publishing, replication studies, and dataset curation
Readership
Academics (scientists, researchers, MSc, and/or PhD students) from the fields of Biomedical Engineering, Computer Science, Biology, and Information Technology. The audience also includes epidemiologists, computational epidemiologists, software engineers, biostatisticians, and data scientists.
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- About the authors
- Chapter 1: Introduction
- Abstract
- 1.1. Preface
- 1.2. Data integration, hypergraphs, and type theory
- 1.3. Philosophy and the semantic web
- 1.4. COVID, philosophy, and science
- 1.5. How cancer, COVID, and cardiac care may accelerate emerging research trends
- 1.6. Navigating the proliferation of research data
- 1.7. Summary
- Part I: Biomedical data formats and data integration
- Chapter 2: Data structures associated with biomedical research
- Abstract
- 2.1. Introduction
- 2.2. Personalized medicine in the context of COVID-19
- 2.3. A review of certain commonly used biomedical data formats
- References
- Chapter 3: Data mining and predictive analytics for cancer and COVID-19
- Abstract
- 3.1. Introduction
- 3.2. Precision medicine and bioimaging
- 3.3. Precision medicine in trial design
- 3.4. Text and data mining via CORD-19
- References
- Chapter 4: Modular design, image biomarkers, and radiomics
- Abstract
- 4.1. Introduction
- 4.2. Image biomarkers (and others) for cardiac and oncology diagnostics
- 4.3. Multi-aspect modular design in a heterogeneous data space
- 4.4. Data integration via multi-aspect modules
- References
- Part II: Type theory, graphs, and conceptual spaces
- Chapter 5: Types' internal structure and “non-constructive” (“NC4”) type theory
- Abstract
- 5.1. Introduction
- 5.2. Types as conceptual structures
- 5.3. Hypergraph ontologies
- References
- Chapter 6: Using code models to instantiate data models
- Abstract
- 6.1. Introduction
- 6.2. Syntagmatic graphs and pointcut expression semantics
- 6.3. Applying pointcut expressions for data modeling
- 6.4. Hypergraph representations for data-persistence bridge code
- References
- Part III: Conceptual spaces and graph-oriented data-modeling paradigms
- Chapter 7: Multi-aspect modules and image annotation
- Abstract
- 7.1. Introduction
- 7.2. Image annotations: Core data models
- 7.3. Annotations and image features
- References
- Chapter 8: Image annotation as a multi-aspect case study
- Abstract
- 8.1. Introduction
- 8.2. Annotations and radiomics
- References
- Chapter 9: Conceptual spaces and scientific data models
- Abstract
- 9.1. Introduction
- 9.2. Verb-centric grammars and information-delta paths
- 9.3. Conceptual and thematic roles
- 9.4. Delta roles and conceptual space markup language
- 9.5. Conclusion: Toward a scientific data semantics
- References
- Index
Product details
- No. of pages: 286
- Language: English
- Copyright: © Academic Press 2022
- Published: January 18, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323851978
- eBook ISBN: 9780323853569
About the Authors
Amy Neustein
Affiliations and Expertise
Nathaniel Christen
Affiliations and Expertise
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