GTGenie

Prediction of biomarker–disease associations based on graph attention network and text representation

Data description

We use HMDAD as example and other datasets files structure are similar.
HMDAD/microbes: ID and names for microbes.
HMDAD/diseases: ID and names for diseases.
HMDAD/adj: interaction pairs between microbes and diseases.
HMDAD/interaction: known microbe-disease interaction matrix.
HMDAD/D_SSM: disease semantic similarity.
HMDAD/M_SSM: microbe semantic similarity.
HMDAD/all_text: text description about disease-microbe associations.
HMDAD/microbe_to_taxon: the taxon of microbes which is used to create microbe semantic similarity.

Text_encoding description

get_text_embedding.py: get text features accroding to 5-fold cross-validation.

Run steps

1. Download biobert_v1.1 from (https://huggingface.co/dmis-lab/biobert-v1.1/tree/main) --- files "pytorch_model.bin", "config.json", and "vocab.txt" are required.
2. Generate text embedding using all_text.csv for each dataset offline.
3. Run main.py to train the model using 5-fold cross-validation.

Requirements

• Pytorch 1.8.1
• tensorflow 1.15
• transformers
• sentencepiece
• numpy
• scipy
• sklearn
• xlrd 1.2.1
• tensorflow-determinism-0.3.0