This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information network of disease genes is constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, we use a multi-layer perceptron model to predict the gene-disease association.
Environment Requirement
The code has been tested running under Python 3.7. The required main packages are as follows:
- torch=1.11
- networkx=2.0
- node2vec=0.4.3
- gensim=3.0.1
- Scikit Learn
- numpy=1.19
- pandas>=1.0
- h5py=2.10
- openssl=1.1
Data The data in this study are derived from the paper "Wang, L., Shang, M., Dai, Q. et al. Prediction of lncRNA-disease association based on a Laplace normalized random walk with restart algorithm on heterogeneous networks. BMC Bioinformatics 23, 5 (2022). ".
Python implementation files for MHAGP
1. line_embedding.py and node2vec_embedding.py are used to extract three network features.
2. feature_concatenate.ipynb - Jupyter notebook for concatenate the gene-disease features of the three networks extracted by line and node2vec algorithm.
3. attenetion_prediction.ipynb- Jupyter notebook for the gene-disease association prediction.
Usage
Code tested only in NVIDIA GeForce RTX 3090.
git clone https://github.com/Bio503/MHAGP.git
In addition, we uploaded our experiments in both the docker hub and the code ocean. The source and download method are as follows:
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docker hub: docker pull bio503/mhagp
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code ocean: https://codeocean.com/capsule/2074362
For any doubts or suggestions please contact： [email protected].