Meta2Vec, an embedding for Metabolomics study
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.
meta2vec package can be installed via
pip install meta2vec
Here is a brief overview of how to use
Load Pre-trained Embeddings
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
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
from meta2vec import most_similar
# Find the most similar HMDB IDs to a given HMDB ID
similar_compounds = most_similar(hmdb_embeddings, "HMDB0000001")
from meta2vec import visualize_umap
# Visualize the embeddings using UMAP
If you would like to contribute to this project, please contact firstname.lastname@example.org
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