Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Mapping the Montessori mathematics curriculum to Dehaene's four pillars of learning. [PDF]
Marshall C +2 more
europepmc +1 more source
LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler +7 more
wiley +1 more source
Audience attitudes and aesthetic perception of digitally empowered contemporary lacquer painting: a survey-based technology-imagery-perception model. [PDF]
Liu Y.
europepmc +1 more source
Accuracy in parameter estimation and simulation approaches for sample-size planning accounting for item effects. [PDF]
Buchanan EM +24 more
europepmc +1 more source
Effects of age and cognitive features on comprehension of healthcare symbols in hospitals in Guangzhou. [PDF]
Deng L, Wang W, Li P.
europepmc +1 more source
A multimodal transformer-based tool for automatic generation of concreteness ratings across languages. [PDF]
Kewenig V, Skipper JI, Vigliocco G.
europepmc +1 more source

