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Online version: OmicLearn

πŸ“° Manual and Documentation: OmicLearn ReadTheDocs

OmicLearn Tests OmicLearn Python Badges OmicLearn Version OmicLearn Release OmicLearn License


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.

Manuscript

πŸ“° Open-access article: Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data

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

🟒 Streamlit share

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:

Windows, macOS, Linux

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.

    1. Redirect to the folder of choice and clone the repository: git clone https://github.com/MannLabs/OmicLearn
    2. Create a new environment for OmicLearn: conda create --name omic_learn python=3.9
    3. Activate the environment with conda activate omic_learn
    4. Change to the OmicLearn directory with cd OmicLearn and install OmicLearn with 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:

OmicLearn Workflow

Detailed instructions on how to get started with OmicLearn can be found here.

On this page, you can click on the titles listed in the Table of Contents, which contain instructions for each section.

Contributing

All contributions are welcome. πŸ‘

πŸ“° 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.

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πŸ§ͺ πŸ–₯ Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

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