Key research themes
1. How can biological ontologies be effectively structured, integrated, and utilized to enhance biomedical data access and interoperability?
This research area focuses on developing frameworks, repositories, and tools that facilitate the creation, management, integration, and semantic querying of biological and biomedical ontologies. The aim is to improve accessibility, interoperability, and consistency of heterogeneous biomedical data across resources and applications, fostering advancements in data-driven biological research and biomedical informatics.
2. What approaches and frameworks enable the creation, evaluation, and refinement of biomedical ontologies for improved quality and usability?
This line of research addresses the methodological and evaluative challenges in biomedical ontology development, seeking strategies to ensure ontologies meet application requirements, maintain consistency, and support complex biological knowledge representation. Topics include evaluation frameworks, quality assurance practices, ontology merging, and the integration of domain expertise to improve ontology accuracy, interoperability, and application effectiveness.
3. How do ontologies specifically enhance the representation, integration, and semantic analysis of biological entities and functions such as genes, chemicals, and cell types?
Research under this theme investigates ontological frameworks and knowledgebases tailored to represent biological functions, molecular entities, chemicals, and neuronal cell types, focusing on structured annotation, causal modeling, and interoperability with experimental data. This includes development of gene function ontologies, chemical functional ontologies, and data-driven cell type ontologies that integrate multimodal information and support hypothesis generation in biology and neuroscience.