Epistatic Kernels for GPU-accelerated Gaussian process regression
EpiK is a Python library designed to infer sequence-function relationships using Gaussian process models. Built on top of GPyTorch and KeOps, EpiK enables fitting these models to large datasets containing hundreds of thousands to millions of sequence measurements.
You can find more detailed documentation and tutorials here
Scripts to reproduce the analyses and figures from the paper are avaiable in a separate repository
- Juannan Zhou, Carlos Martí-Gómez, Samantha Petti, David M. McCandlish. Learning sequence-function relationships with scalable, interpretable Gaussian processes (2025) In preparation.
Installation
We recommend using an new independent environment with python3.8, as used during
development and testing of EpiK to minimize problems with dependencies. Create a python3 conda environment and activate it
conda create -n epik python=3.8
conda activate epik
Users
Install with pip
pip install epik
Developers
Download the repository using git and cd into it
git clone git@github.com:cmarti/epik.git
Install repository
cd epik
pip install .
Run tests with pytest
pytest test
1 maintainer
Metadata
1 maintainer
Release files for epik 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| epik-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for epik-0.1.0-py3-none-any.whl |
Total release size: 57.9 kB
Release files / epik-0.1.0.tar.gz
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| Tags | Python 3 |
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