AxoWise is a web service tool engineered to convert complex networks of knowledge into contextual insights through interactive visualization and AI techniques.
"Knowledge is the first concern of the scientist, but wisdom is the ultimate intellectual goal of us all." — The Stress of Life, Hans Selye, p. 404.
AxoWise is designed around the theme of "Bridging Knowledge and Wisdom through Networks." Inspired by the adaptability and regenerative capabilities of the axolotl, the tool symbolizes the transformation of isolated data points into interconnected, contextual insights. Just as the axolotl adapts and regenerates, AxoWise adapts to diverse layers of knowledge—such as biological networks, networks of functional terms, and networks of publications—integrating them into a cohesive whole. This process of connecting and integrating fragmented data points within their relevant contexts enables the discovery of holistic insights, much like how the brain’s neural pathways create complex understanding through their interconnectedness.
AxoWise is actively under development, with the current version still evolving to enhance biological network analysis. At this stage, it integrates protein interaction networks with functional term networks and publication abstracts, creating interconnected layers of knowledge. While the platform already provides valuable insights into biological interactions and functions, it remains a work in progress, with ongoing efforts focused on expanding features, improving data integration, and refining analytical capabilities. Future updates will continue to enhance its functionality and usability.
The Protein Graph Database is organized into several directories:
- docs contains files used for documentation, including images or text files.
- backend/src contains the Flask server code, which serves all files and handles requests to the database.
- backend/gephi contains the code to pre-organize the data into nodes, which can then be used in the frontend. It is used as a submodule in the server.
- backend/test_data contains test data.
- frontend contains the Vue frontend code used to visualize the protein data.
- scrapting/KEGG contains the code used to fetch data from the KEGG source. This directory is only used as a reference for future scraping.
To use the Protein Graph Database, follow these steps:
- Follow the Installation Guide
- Run the project by executing the following commands:
make neo4j(starts Neo4J database)conda activate pgdb(activates the conda environment)make build(builds the entire project)make start(runs the project)