NLM DIR Seminar Schedule
UPCOMING SEMINARS
-
July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
-
July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
Scheduled Seminars on July 3, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Ontologies have played a critical role in the last 25 years in knowledge representation, data indexing and retrieval, and data integration. Projects like the Gene Ontology and the Human Phenotype Ontology, coordinated through the Open Biomedical Ontology (OBO) Foundry, demonstrate the ability to encode knowledge in a computable format and link it to external sources of knowledge from other domains at scale. As the volume of data used in biomedical experiments grows, efforts to improve data stewardship, such as the FAIR principles, have identified ontologies as a key tool for making data and metadata machine actionable. In parallel with this are efforts to produce computable knowledge, defined as knowledge that is explicitly represented such that it can be parsed and reasoned upon using computational methods to derive new knowledge, and represent it in resource like knowledge graphs. In this presentation, I provide background on what ontologies are and how they are used, and introduce the Cell Knowledge Network—a DIR-funded knowledge network designed to connect data and knowledge about cell phenotypes to knowledge about anatomy, drug targets, human phenotypes and disease, and other domains.