semvec/pypi-packages huggingface dataset:
meta2vec 0.2.0
meta2vec, an embedding for Metabolomics study

# meta2vec

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`meta2vec` is a Python package for metabolite embedding, which allows for the representation of metabolites in a vector space.

`meta2vec` package contains three modules:

* `distance`: provides functions for calculating the similarity distance between two metabolites using their embeddings.
* `utils`: provides helper functions for working with HMDB (Human Metabolome Database) dataset.
* `visualize`: provides functions for visualizing the embeddings using UMAP.

An example.ipynb is provides.

## Installation

`meta2vec` package can be installed via `pip`:

```bash
pip install meta2vec
```

## Usage

Here is a brief overview of how to use `meta2vec` package:

### Load Pre-trained Embeddings

```python
from meta2vec import from_pretrained

# Load the TransE pre-trained embeddings
hmdb_embeddings = from_pretrained(model_type="TransE", embedding_dir="embedding")
```

The above code loads the pre-trained HMDB embeddings from the `embedding` directory. If the embeddings are not already present in the directory, the function will download them from the GitHub repository.

### Calculate Distance between Two Metabolites

```python
from meta2vec import hmdb_id_cosine_distance, hmdb_id_euclidean_distance

# Calculate the cosine distance between two HMDB IDs
cosine_distance = hmdb_id_cosine_distance(hmdb_embeddings, "HMDB0000001", "HMDB0000002")

# Calculate the Euclidean distance between two HMDB IDs
euclidean_distance = hmdb_id_euclidean_distance(hmdb_embeddings, "HMDB0000001", "HMDB0000002")
```

### Find Most Similar Metabolites

```python
from meta2vec import most_similar

# Find the most similar HMDB IDs to a given HMDB ID
similar_compounds = most_similar(hmdb_embeddings, "HMDB0000001")
```

### Visualize Embeddings

```
from meta2vec import visualize_umap

# Visualize the embeddings using UMAP
visualize_umap(hmdb_embeddings)
```

## Contributing

If you would like to contribute to this project, please contact [yxlu0613@gmail.com](yxlu0613@gmail.com)