https://github.com/gcorso/neuroseed

Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch (NeurIPS 2021)
https://github.com/gcorso/neuroseed

bioinformatics biological-sequences hierarchical-clustering machine-learning multiple-sequence-alignment neurips-2021 pytorch

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Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch (NeurIPS 2021)

• Host: GitHub
• URL: https://github.com/gcorso/neuroseed
• Owner: gcorso
• License: mit
• Created: 2021-04-27T14:11:39.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2023-10-14T01:15:09.000Z (about 1 year ago)
• Last Synced: 2024-05-06T00:03:08.712Z (7 months ago)
• Topics: bioinformatics, biological-sequences, hierarchical-clustering, machine-learning, multiple-sequence-alignment, neurips-2021, pytorch
• Language: Python
• Homepage: https://arxiv.org/abs/2109.09740
• Size: 1.38 MB
• Stars: 66
• Watchers: 2
• Forks: 16
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Neural Distance Embeddings for Biological Sequences

Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch published at NeurIPS 2021 ([preprint](https://arxiv.org/abs/2109.09740)). NeuroSEED is a novel framework to embed biological sequences in geometric vector spaces.

![diagram](./tutorial/cover.png)

Note: unfortunately due to my move between institutions the download scripts are broken and the files are no longer available on the original Drive. I have reuploaded them [here](https://drive.google.com/drive/folders/1tmXtsUV3MwxIDr-NB8Uk78IoCkBZtiu_?usp=sharing), but reach out if you believe there are some missing files.

## Overview

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 

## Installation

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 setup.py build_ext --inplace; cd ../../..

cd hierarchical_clustering/relaxed/unionfind; python setup.py build_ext --inplace; cd ../../..

```

## Reference

```

@article{corso2021neuroseed,

  title={Neural Distance Embeddings for Biological Sequences},

  author={Corso, Gabriele and Ying, Rex and P{\'a}ndy, Michal and Veli{\v{c}}kovi{\'c}, Petar and Leskovec, Jure and Li{\`o}, Pietro},

  journal={Advances in Neural Information Processing Systems},

  year={2021}

}

```

## License

MIT