Predicting disease-metabolite associations based on the metapath aggregation of tripartite heterogeneous networks

# MAHN for disease-metabolite associations prediction

## Dependecies

- Python 3.9

- pytorch 1.12.1

- dgl 1.1.1

- numpy 1.22.4

- pandas 1.4.4

## Dataset

disease-metabolite associations:association_DME.xlsx

disease-microbe associations:association_DMI.xlsx

microbe-metabolite associations:association_MIME.xlsx

disease semantic networks based on metapath DMED and DMID:A_DMED.xlsx and A_DMID.xlsx

metabolite semantic networks based on metapath MEDME and MEMIME: A_MEDME.xlsx and A_MEMIME.xlsx 

Disease Gaussian kernel similarity:disease_Gaussian_Simi.xlsx

Disease semantic similarity:disease_Semantic_simi.xlsx

Metabolite functional similarity:metabolite_func_simi.xlsx

Metabolite Gaussian kernel similarity:metabolite_Gaussian_Simi.xlsx

microbe Gaussian kernel similarities:microbe_Gaussian_Simi_1.xlsx and microbe_Gaussian_Simi_2.xlsx 

###### Model options

```

--epochs           int     Number of training epochs.                 Default is 1000.

--attn_size        int     Dimension of attention.                    Default is 64.

--attn_heads       int     Number of attention heads.                 Default is 6.

--out_dim          int     Output dimension after feature extraction  Default is 64.

--dropout          float   Dropout rate                               Default is 0.1.

--slope            float   Slope                                      Default is 0.2.

--lr               float   Learning rate                              Default is 0.001.

--wd               float   weight decay                               Default is 5e-3.

```

###### How to run?

```

Run main.py