Large Language Model-based creation of knowledge graph from a publication

This project proposes the development of a Python web application that utilizes Large Language Models (LLMs) for extracting, transforming, and visualizing molecular interaction networks from scientific literature. Leveraging LLMs' advanced natural language processing capabilities, the application will interpret complex academic texts to construct interaction graphs. These results would be uploaded to the Network Data Exchange (NDEx) platform, enabling researchers to manage and visualize biological data more effectively.

Key deliverables include: An interactive Python web app for document processing and network generation. Seamless integration with the NDEx API for data management and graph visualization using Cytoscape. A robust pipeline for document analysis and data transformation with CX2 format output.