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https://github.com/Novartis/ChemBioMultimodalAutoencoders

a package for streamlined multidomain data integration and translation based on cross-modal autoencoders architectures
https://github.com/Novartis/ChemBioMultimodalAutoencoders

Last synced: 13 days ago
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a package for streamlined multidomain data integration and translation based on cross-modal autoencoders architectures

Host: GitHub
URL: https://github.com/Novartis/ChemBioMultimodalAutoencoders
Owner: Novartis
License: mit
Created: 2023-03-16T13:01:54.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-07-05T14:07:40.000Z (over 1 year ago)
Last Synced: 2025-01-08T19:26:56.674Z (23 days ago)
Language: Python
Size: 102 KB
Stars: 28
Watchers: 7
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # ChemBioMultimodalAutoencoders

 



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| --- | --- |

| CI/CD | [![tests](https://github.com/Novartis/ChemBioMultimodalAutoencoders/actions/workflows/python-package-tests.yml/badge.svg?branch=main)](https://github.com/Novartis/ChemBioMultimodalAutoencoders/actions/workflows/python-package-tests.yml) [![builds](https://github.com/Novartis/ChemBioMultimodalAutoencoders/actions/workflows/build-and-publish.yml/badge.svg)](https://github.com/Novartis/ChemBioMultimodalAutoencoders/actions/workflows/build-and-publish.yml) |

| Package | [![PyPI - Version](https://img.shields.io/pypi/v/multimodal-autoencoders.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/multimodal-autoencoders/) ![Downloads](https://static.pepy.tech/badge/multimodal-autoencoders)[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/multimodal-autoencoders.svg?logo=python&label=Python&logoColor=gold)](https://pypi.org/project/multimodal-autoencoders/) |

| Meta | [![Hatch project](https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg)](https://github.com/pypa/hatch) [![code style - black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![types - Mypy](https://img.shields.io/badge/types-Mypy-blue.svg)](https://github.com/python/mypy) [![imports - isort](https://img.shields.io/badge/imports-isort-ef8336.svg)](https://github.com/pycqa/isort) [![LICENSE](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/Novartis/ChemBioMultimodalAutoencoders/blob/readme-update/license.txt)

 



a package for streamlined multidomain data integration and translation based on [cross-modal autoencoder architecture](https://github.com/uhlerlab/cross-modal-autoencoders)[[1]](https://github.com/Novartis/ChemBioMultimodalAutoencoders#references). It is designed to add new data modalities and train models for seamless translation. 

# Installation

To install the package, simply run:

```pip install multimodal-autoencoders```

**(optional)**

To make sure you have all the dependencies, you can create an appropriate environment using the environment.yml file with Conda:

```conda env create -f environment.yml```

# Usage

An example on how to train and use the multimodal autoencoders can be found in relevant notebooks in `examples` 





Usage is centered around a JointTraner instance (defined in multimodal_autoencoders/trainer/joint_trainer.py). A central part of the whole architecture is that different components need to be associated to the individual modalities. This is done through python dictionaries, with which most users will be familiar with.


# Authors

**Thibault Bechtler** ([email protected]) & **Bartosz Baranowski** ([email protected])

Contributors:

**Michal Pikusa** ([email protected]), **Steffen Renner** ([email protected])

# References

```[1] Yang, K.D., Belyaeva, A., Venkatachalapathy, S. et al. Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nat Commun 12, 31 (2021). https://doi.org/10.1038/s41467-020-20249-2```