Pipeline for Interpretable Classification on Omics
The PICO is a tool to apply machine learning algorithms to omics datasets. 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.
More generally, it is a tool to apply interpretable machine learning algorithms to tabular data. It focuses on analyzing the features selected by the models.
You can find the documentation here. It explains how to use PICO but also how it works.
- Élina Francovic-Fontaine
- Vincent Primpied
- Vincent Vilain
- Gabriel Leclerc
- Thierry Moszkowicz
- Mathieu Bazinet
- Thibaud Godon
- Pier-Luc Plante
- Baptiste Bauvin
- Louis-Philippe Vignault
PICO 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".
Clone the project with :
git clone https://github.com/ElinaFF/PICO.gitIt is recommanded to setup a virtual environment. When it's done, use your isolated python and install pico package locally and in editable mode with :
python -m pip install -e ".[dev]"Pipeline for Interpretable Classification on Omics (PICO) © 2025 by Elina Francovic-Fontaine is licensed under CC BY-NC-SA 4.0