Helical provides a framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.
Helical simplifies the entire application lifecycle when building with bio foundation models. You will be able to:
- Leverage the latest bio foundation models through our easy-to-use python package
- Run example notebooks on key downstream tasks from examples
We will update this repo on a regular basis with new models, benchmarks, modalities and functions - so stay tuned. Let’s build the most exciting AI-for-Bio community together!
We recommend installing Helical within a conda environment with the commands below (run them in your terminal) - this step is optional:
conda create --name helical-package python=3.11.8
conda activate helical-package
To install the latest pip release of our Helical package, you can run the command below:
pip install helical
To install the latest Helical package, you can run the command below:
pip install --upgrade git+https://github.com/helicalAI/helical.git
If you desire to run your code in a singularity file, you can use the singularity.def file and build an apptainer with it:
apptainer build --sandbox singularity/helical singularity.def
and then shell into the sandbox container (use the --nv flag if you have a GPU available):
apptainer shell --nv --fakeroot singularity/helical/
To run examples, be sure to have installed the Helical package (see Installation) and that it is up-to-date.
You can look directly into the example folder above and download the script of your choice, look into our documentation for step-by-step guides or directly clone the repository using:
git clone https://github.com/helicalAI/helical.git
Within the examples/notebooks folder, open the notebook of your choice. We recommend starting with Quick-Start-Tutorial.ipynb
| Example | Description | Colab |
|---|---|---|
| Quick-Start-Tutorial.ipynb | A tutorial to quickly get used to the helical package and environment. |  |
| Geneformer-vs-UCE.ipynb | Zero-Shot Reference Mapping with Geneformer & UCE and compare the outcomes. | |
| Hyena-DNA-Inference.ipynb | An example how to do probing with HyenaDNA by training a neural network on 18 downstream classification tasks. | |
| Cell-Type-Annotation.ipynb | An example how to do probing with scGPT by training a neural network to predict cell type annotations. | |
| Coming Soon | New models such as SCimilarity, scVI; benchmarking scripts; new use cases; others |
We are eager to help you and interact with you. Reach out via support@helical-ai.com. You can also open github issues here.
If you are (or plan to) working with bio foundation models s.a. Geneformer or UCE on RNA and DNA data, Helical will be your best buddy! We provide and improve on:
- Up-to-date model library
- A unified API for all models
- User-facing abstractions tailored to computational biologists, researchers & AI developers
- Innovative use case and application examples and ideas
- Efficient data processing & code-base
We will continuously upload the latest model, publish benchmarks and make our code more efficient.
A lot of our models have been published by talend authors developing these exciting technologies. We sincerely thank the authors of the following open-source projects:
You can find the Licenses for each model implementation in the model repositories:
Please use this BibTeX to cite this repository in your publications:
@software{allard_2024_13135902,
author = {Putzeys, Benoit and
Sbaï, Jad and
Wood, Matthew and
Schneider, Rick and
Quiroga Londoño, Mariana and
Klop, Mathieu and
Allard, Maxime},
title = {helicalAI/helical: v0.0.1-alpha3},
month = jul,
year = 2024,
publisher = {Zenodo},
version = {0.0.1a3},
doi = {10.5281/zenodo.13135902},
url = {https://doi.org/10.5281/zenodo.13135902}
}