About MGREL

this repository store the code for paper MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction。

we first re-organized the current gene-disease association prediction benchmark by extracting the latest gene-disease associations from the OMIM database. Then, we developed a multi-graph representation learning-based ensemble model, named MGREL to predict potential gene-disease associations. MGREL integrated two channels to extract gene and disease features, including knowledge extraction channel and graph learning channel. Then ensemble machine learning methods were used as the classifier to predict the association. The workflow of MGREL model was shown in Figure.

Data

We adopted and reorganized a gene-disease association benchmark dataset as widely used in the previous studies RGCN AND LUPI, which consists of gene-disease associations, gene-gene associations, disease-disease associations, gene features, and disease features (as shown in Table). See more detials in the paper.

Data	Source	Num	Edges	Dementions
Association
Gene-disease	CTD		22054	-
Gene-Gene	HumanNet	12331	733836	-
Disease-Disease	MinMiner	3215	645945	-
Features
Gene features	Microarray	-	-	4536
Gene features	gene-phenotype association	-	-	12944
Disease features	DO similarity	-	-	16592
Disease features	TF-IDF of OMIM text	-	-	16592

The sum dementions of DO similarity, TF-IDF of OMIM text is 16592

SubFolders

• baseline：code for HGT, HAN, LUPU, RGCN, PGCN

• data：store the data updated from OMIM.

  • dd.npz, gd.npz, gg.npz was the final dataset generated by MGREL from clinicalfeatures_tfidf.mat，genes_phenes.mat，GeneFeatures.mat files.
  • EntreID.txt, diseaseID.txt are the geneID and diseaseID extrated from LUPI dataset.
  • geneDisAdj_sum.pkl is the association prepcessed from OMIM and saved in crs spares matrix. To avoid the conflict, we match the association and ignore the non-association diseases or genes, and fill it with 0. After removing redundancy and missing data, the final version of our updated gene-disease associations contains 12,331 genes, 3,215 diseases, and 22,054 associations.See more details in the paper.

• src：

  • model：code for MGREL

• OpenNE：See the repository OpenNE. Added the running shell to make code read friendly.

• run.txt：Log for every running CMD and log date

Code usage

1. get these 3 data files clinicalfeatures_tfidf.mat，genes_phenes.mat，GeneFeatures.mat from PGCN OR LUPI

2. get OpenNE environments to the same conda env

3. change dir to /MGREL and running /src/model/main.py

cd YOUR_PAHT/MGREL
python src/model/main.py -ah 512 64 -id 1 -md train -fo 5 -se 0 -om SDNE HOPE

Reference

If you are interested in our work, please see details in this artical and cite it. Thanks!

MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction

Acknowledgement

To succeed in MGREL model is This project could not be developed without the support of collaborators gu-yaowen We also thank the open source OMIM database, the NCBI database and OpenNE repository.