Skip to main content

Metabolomic Dashboard for Interpretable Classification

This project has been archived.

The maintainers of this project have marked this project as archived. No new releases are expected.

Project description

MeDIC

Metabolomic Dashboard for Interpretable Classification

Description

The MeDIC is a tool to apply machine learning algorithms to untargeted metabolomics datasets acquired by liquid-chromatography mass spectrometry. The goal is to extract the most important features because they are potential novel biomarkers. The interface is made to be easy to use and intuitive even for those with small to nonexistant experience in programming and AI.

The documentation

You can find the documentation here. It explains how to use MeDIC but also how it works.

Authors and contributors

Disclaimer

MeDIC is still in development. If you encounter any issue or have any suggestion, feel free to contact us at elina.francovic-fontaine.1@ulaval.ca. Or you can leave an issue here with the tag "bug".

Development

Setup

Clone the project with :

git clone https://github.com/ElinaFF/MeDIC.git

It is recommanded to setup a virtual environment. When it's done, use your isolated python and install medic package locally and in editable mode with :

python -m pip install -e ".[dev]"

Trigger a release

Let's say you want to update to version 1.3.2.

  1. Make sure the main branch is working fine, either run pytest locally or trigger a tests workflow manually.
  2. Set the version to __version__ = "1.3.2" in medic/init.py (you can edit a file from GitHub by clicking on the key . on your keyboard)
  3. Commit the new version change with git add medic/__init__.py and git commit -m "Bump version
  4. Push the commit with git push
  5. Go to https://github.com/ElinaFF/MeDIC/releases/new, choose a tag, create new tag with name 1.3.2
  6. Document what have changed since the last release (you can try the Generate release notes button)
  7. Click Publish release!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

medic_ml-0.2.0.tar.gz (340.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

medic_ml-0.2.0-py3-none-any.whl (354.7 kB view details)

Uploaded Python 3

File details

Details for the file medic_ml-0.2.0.tar.gz.

File metadata

  • Download URL: medic_ml-0.2.0.tar.gz
  • Upload date:
  • Size: 340.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.28.1

File hashes

Hashes for medic_ml-0.2.0.tar.gz
Algorithm Hash digest
SHA256 79c5be28ef54ec8bd4d0988cca9d06d51636b1845928e1fb380ea768570de15a
MD5 37e32a4be0f02422372cf71b1805d088
BLAKE2b-256 a6a91205925646e40e3dae6d0a2fed814ede0fe396b3e88536dafcf1c7929ed5

See more details on using hashes here.

File details

Details for the file medic_ml-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: medic_ml-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 354.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-httpx/0.28.1

File hashes

Hashes for medic_ml-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 f27d0c4baa849344a3af10ebc51b442de1c4a3084bb3f701e0639d60eea0d2aa
MD5 7362e4de81d2f49dc4fe7bc8eb4f8704
BLAKE2b-256 9ba0823f476dec02bae67163307364ab84a687a89ec873f5a3eb238a28a090d0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page