Online version: OmicLearn
📰 Manual and Documentation: OmicLearn ReadTheDocs
OmicLearn¶
Transparent exploration of machine learning for biomarker discovery from proteomics and omics data. This is a maintained fork from OmicEra.
Quickstart¶
A three-minute quickstart video to showcase OmicLearn can be found here.
PyCon Talk¶
Manuscript¶
OmicLearn was featured as a supplementary cover of the Special Issue on Software Tools and Resources of the Journal of Proteome Research!
Citation:¶
Torun, F. M., Virreira Winter, S., Doll, S., Riese, F. M., Vorobyev, A., Mueller-Reif, J. B., Geyer, P. E., & Strauss, M. T. (2022).
Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data.
Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.2c00473
Online Access¶
This is an online version hosted by streamlit using free cloud resources, which might have limited performance. Use the local installation to run OmicLearn on your own hardware.
Local Installation¶
One-click Installation¶
You can use the one-click installer to install OmicLearn as an application locally. Click on one of the links below to download the latest release for:
For detailed installation instructions of the one-click installers refer to the documentation.
Python Installation¶
It is strongly recommended to install OmicLearn in its own environment using Anaconda or Miniconda.
Redirect to the folder of choice and clone the repository:
git clone https://github.com/MannLabs/OmicLearn
Create a new environment for OmicLearn:
conda create --name OmicLearn python=3.10 -y
Activate the environment:
conda activate OmicLearn
Change to the OmicLearn directory and install OmicLearn:
cd OmicLearn pip install .
After a successful installation, type the following command to run OmicLearn:
python -m omiclearn
After starting the Streamlit server, the OmicLearn page should be automatically opened in your browser (Default link:
http://localhost:8501
Getting Started with OmicLearn¶
The following image displays the main steps of OmicLearn:
Detailed instructions on how to get started with OmicLearn can be found here.
Contributing¶
All contributions are welcome. 👍
📰 To get started, please check out our CONTRIBUTING
guidelines.
When contributing to OmicLearn, please open a new issue to report the bug or discuss the changes you plan before sending a PR (pull request).
We appreciate community contributions to the repository.