Knowledge graph informed AI for disease research

A framework for applying best in class knowledge graph and retrieval augmented generation techniques to biomedical data and user queries. This project harnesses code from papers in a way that makes comparison on test data across different approaches possible. It offers portable Python tooling to make building and observing your AI experiments a breeze.

This Python package can be installed locally. First, clone this repository:

$ git clone git@github.com:keppy/disease-lab.git

For now you will need poetry installed to build the package: https://python-poetry.org

Once Poetry is installed, you can install this package locally by running:

$ poetry install

Next, in your Python notebook or script, import the entity extractor:

from disease_lab.entity_extraction.disease_query import expand_disease_query

Currently you can run the interactive command line tool and extract diseases from a text input string

To install dependencies and start the CLI:

$ poetry install
$ disease-lab

If you want to use an open source model, make sure Ollama is installed, updated, and running, and then execute:

$ disease-lab llama3

You will be presented with a prompt > , enter a string containing multiple diseases to get back the entities. Note that this will not return other entities like genes or drug compounds.

https://arxiv.org/abs/2311.17330

https://arxiv.org/abs/2405.14831

TODO: give credit to specific techniques from papers and isolate them for testing