Advancements in Biomedical Data Curation: A Comprehensive Analysis

Scientific research and translation medicine have recently placed a significant emphasis on computational methodology and data curation across various disciplines. This is primarily to advance underlying science and to establish precision-medicine protocols in both laboratory and clinical settings. The intersection of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a unique context within which to examine the theory and practice of biomedical data curation.

Embracing Multiple Perspectives in Data Modeling

Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives. Data modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On one hand, data models are designed for use by computer software components and are consequently constrained by the mechanistic demands of software environments. Data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. On the other hand, data modeling is also constrained by human conceptual tendencies, because the information managed by databases and data networks is ultimately intended to be visualized and utilized by humans as the end-user.

Formal and Humanistic Methodology in Data Modeling

Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume.

Key Features of the Book

This comprehensive volume provides:

• Analysis of how conceptual spaces and related cognitive linguistic approaches can inspire programming and query-processing models.
• Overview of the vital role that data modeling/curation has played in significant medical breakthroughs.
• Case studies of data-modeling in concrete scientific practice, particularly in the context of precision medicine.
• Applications of image analysis and computer vision in the context of precision medicine.
• Examination of the role of technology in scientific publishing, replication studies, and dataset curation.

Conclusion

Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care serves as a valuable resource for researchers, scientists, and healthcare professionals seeking to understand the complex interplay between data modeling, computational methodology, and precision medicine. By exploring the multifaceted nature of biomedical data curation, this book provides insights into the future of scientific research and translation medicine.