Publications by authors named "D Osumi-Sutherland"
Article Synopsis
- The use of well-structured ontologies and ontology-aware tools enhances data and analyses to be FAIR (Findable, Accessible, Interoperable, Reusable), supporting effective lexical searches and biologically meaningful annotation grouping.
- Researchers face challenges in adopting ontologies, primarily due to their complexity and the tendency to create simplified hierarchies that may misuse relationship types, leading to ineffective organization.
- A suite of validation tools is introduced to help users align their hierarchies with established ontology structures, providing graphical reports and tailored views for various atlases like the HuBMAP Human Reference Atlas and the Human Developmental Cell Atlas.
Article Synopsis
- Ontologies are key for managing consensus knowledge in areas like biomedical, environmental, and food sciences, but creating and maintaining them requires significant resources and collaboration among experts.
- The Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI) leverages Large Language Models and Retrieval Augmented Generation to automate the generation of ontology components, showing high precision in relationship creation and ability to produce acceptable definitions.
- While DRAGON-AI can significantly support ontology development, expert curators remain essential for overseeing the quality and accuracy of the generated content.
Article Synopsis
- Phenotypic data helps us understand how genomic variations affect living organisms and is vital for clinical applications like diagnosing diseases and developing treatments.
- The field of phenomics aims to unify and analyze the vast amounts of phenotypic data collected over time, but faces challenges due to inconsistent methods and vocabularies used to record this information.
- The Unified Phenotype Ontology (uPheno) framework offers a solution by providing a standardized system for organizing phenotype terms, allowing for better integration of data across different species and improving research on genotype-phenotype associations.
Background: Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools.
View Article and Find Full Text PDFBridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research.
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