Levels and building blocks-toward a domain granularity framework for the life sciences.

Area of Science:

• Life Sciences
• Bioinformatics
• Data Science

Background:

• High-throughput technologies and Big Data necessitate computer-parsable data standards and online repositories.
• An integrated system of hierarchical levels is crucial for organizing, structuring, and integrating data from disparate sources.
• Theories of granularity offer a solution for creating such integrated systems.

Purpose of the Study:

• To address shortcomings in existing applications of the concept of levels in data organization.
• To propose a general theory of granularity that circumvents these problems.
• To introduce a domain granularity framework for the life sciences.

Main Methods:

• Formal approaches to theories of granularity from information science and ontology research.
• Introduction of 'building blocks' to create a formally characterized hierarchy of levels.
• Integration of this hierarchy as an organizational backbone for other hierarchies.

Main Results:

• A general theory of granularity proposed by Keet circumvents identified shortcomings.
• A hierarchy of levels, formally characterized by Keet's theory, is introduced.
• A domain granularity framework for the life sciences is developed, impacting the Basic Formal Ontology's 'material entity' category.

Conclusions:

• The proposed framework provides a basis for a comprehensive life sciences information system.
• It facilitates intuitive data structuring, exploration, and reasoning across granularity levels.
• The framework offers a methodological basis for data set comparability and semantic similarity measurement.