EGOAL is a attempt of introducing the Holy Grail of Artificial Intelligence — combination of Machine Learning and Symbolic Reasoning, aka. Neural-Symbolic AI — into the sight of Synthetic Biology.
We are trying to assist researches and drive innovations in SynBio, via Abductive Learning, the novel paradigm of neural-symbolic AI proposed by researchers from Nanjing University.
This project is a part of Drylab for iGEM team Nanjing-China 2024.
This is also a project of 2024 AI+ Contest, School of Artificial Intelligence, Nanjing University.
We constructed a gene expression prediction model based on Abductive Learning, a prominent neural-symbolic AI paradigm. By leveraging the Gene Ontology, it generates accurate predictions with minimal reliance on historical data, demonstrating the effectiveness of neural-symbolic AI in the field of synthetic biology.
The knowledge base, i.e. Gene Ontology, contains the relations of gene regulation in its graph structure, which can be learned by the model. Thus we can predict expression of genes on the regulation pathway from the condition and result of the experiment.
git clone git@github.com:yfxiang0112/EGOAL
cd EGOAL
pip install -r requirements.txt
python src/predict/predict.py -d True -i examples/NADK/input_terms.txt -o examples/NADK
NOTE: files required for natural language embedding dataset/embedding/go_txt_embd.npy
and dataset/embedding/go_embd_idx.txt are not in the repository due to excessed size.
python src/predict/predict.py -i examples/GAPDH/input.txt -o examples/GAPDH
python src/predict/ui.py