Search · four archives
Search · four archives
6 papers · ranked by Valyu relevance
Amy K. Glen, Chunyu Ma, Luis Mendoza, Finn Womack + 11 more
With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but…
Amy K. Glen, Eric W. Deutsch, Stephen A. Ramsey
Knowledge graphs are increasingly being used to integrate heterogeneous biomedical knowledge and data. General-purpose graph database management systems such as Neo4j are often used to host and search knowledge graphs, but such tools come with overhead and leave biomedical-specific standards compliance and reasoning to…
E. C. Wood, Amy K. Glen, Lindsey G. Kvarfordt, Finn Womack + 13 more
Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to…
Yichun Feng, Lu Zhou, Yikai Zheng, Ruikun He + 2 more
In recent years, Large Language Models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. We developed the Knowledge Graph-based Thought (KGT) framework, an innovative solution…
Guangrong Qin, Kamileh Narsinh, Qi Wei, Jared C. Roach + 15 more
As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such…
David Geleta, Andriy Nikolov, Gavin Edwards, Anna Gogleva + 10 more
The use of knowledge graphs as a data source for machine learning methods to solve complex problems in life sciences has rapidly become popular in recent years. Our Biological Insights Knowledge Graph (BIKG) combines relevant data for drug development from public as well as internal data sources to provide insights for…