We develop a heterogeneous disease-gene-related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred). Based on this framework, a low-dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR-based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease-gene network (RW-RDGN), to predict disease genes with high performance.

• Python >= 3.5
• gensim=3.8
• network>=2.3
• numpy>=1.17

• Step (1): run n2v/e2v_walks.py
  • Input file: data/edges_example.txt (i.e., all edges of a heterogeneous network)
  • Output file: data/walks.txt (i.e., the result of random walks)
• Step (2): run n2v/learn_vecs.py
  • Input file: data/walks.txt (i.e., the result of random walks)
  • Output files:
    • data/emb.txt (i.e., embedding vectors of nodes, text format)
    • data/emb.model (i.e., embedding vectors of nodes, binary format)

Run prediction/dis_gene_pred.py

• Input files:
  • data/cv10_of0.txt (i.e., train and test data)
  • data/nodes.txt (i.e., nodes in network)
  • data/emb.model (i.e., embedding vectors of nodes, binary format)
• Output file:
  • data/prediction_results.txt (i.e., prediction results for disease genes)

If you find HerGePred useful for your research, please consider citing the following paper:

@article{Yang2018HerGePred,
   author = {Yang, Kuo and Wang, Ruyu and Liu, Guangming and Shu, Zixin and Wang, Ning and Zhang, Runshun and Yu, Jian and Chen, Jianxin and Li, Xiaodong and Zhou, Xuezhong},
   title = {HerGePred: heterogeneous network embedding representation for disease gene prediction},
   journal = {IEEE Journal of Biomedical and Health Informatics},
   volume = {23},
   number = {4},
   pages = {1805-1815},
   year = {2018},
   type = {Journal Article}
}

K. Yang, R. Wang, G. Liu, Z. Shu, N. Wang, R. Zhang, J. Yu, J. Chen, X. Li, X. Zhou*. HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction, IEEE Journal of Biomedical and Health Informatics, 2018, 23(4): 1805-1815.