Neural Distance Embeddings for Biological Sequences

Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch. NeuroSEED is a novel framework to embed biological sequences in geometric vector spaces. Preprint will we published soon.



The repository is organised in four main folders one for each of the tasks analysed. Each of these contain scripts and models used for the task as well as instructions on how to run them and the tuned hyperparameters found.

  • edit_distance for the edit distance approximation task
  • closest_string for the closest string retrieval task
  • hierarchical_clustering for the hierarchical clustering task, further divided in relaxed and unsupervised for the two approaches explored
  • multiple_alignment for the multiple sequence alignment task, further divided in guide_tree and steiner_string
  • util contains a series of utility routines shared between all the tasks
  • tests contains a wide range of tests for the various components of the repository


Create a virtual (or conda) environment and install the dependencies:

python3 -m venv neuroseed
source neuroseed/bin/activate
pip install -r requirements.txt

Then install the mst and unionfind packages used for the hierarchical clustering:

cd hierarchical_clustering/relaxed/mst; python build_ext --inplace; cd ../../..
cd hierarchical_clustering/relaxed/unionfind; python build_ext --inplace; cd ../../..