Epistemic AI platform accelerates innovation by connecting biomedical knowledge
This addresses the challenge of efficiently navigating complex biomedical knowledge for researchers, though it appears incremental as it combines existing methods into a new platform.
The paper tackles the problem of information overload and missed connections in biomedical research by introducing the Epistemic AI platform, which uses knowledge graphs, NLP, and network analysis to accelerate discovery and reduce costly mistakes.
Epistemic AI accelerates biomedical discovery by finding hidden connections in the network of biomedical knowledge. The Epistemic AI web-based software platform embodies the concept of knowledge mapping, an interactive process that relies on a knowledge graph in combination with natural language processing (NLP), information retrieval, relevance feedback, and network analysis. Knowledge mapping reduces information overload, prevents costly mistakes, and minimizes missed opportunities in the research process. The platform combines state-of-the-art methods for information extraction with machine learning, artificial intelligence and network analysis. Starting from a single biological entity, such as a gene or disease, users may: a) construct a map of connections to that entity, b) map an entire domain of interest, and c) gain insight into large biological networks of knowledge. Knowledge maps provide clarity and organization, simplifying the day-to-day research processes.