Feature Selection for Bioinformatics with Python

This repository regroups works on the Feature Selection for Bioinformatics with Python course of the SIB.

pre-requisites

The course is targeted to life scientists who are already familiar with the Python programing language and who have good grasp of statistics and Machine Learning core concepts.

In order to follow the course you need to have installed python and jupyter notebooks (or a similar IDE able to handle jupyter notebooks such as VSCode).

environment setup

See the dedicated environment setup file.

course organization

The course is organized in several section where we alternate theory presented in slides (in the slides/ folder) and notebooks (in the notebooks/ folder).

• chapter1_univariate_filtering.ipynb[notebooks/chapter1_univariate_filtering.ipynb]
• chapter1.5_removing_colinear_variables.ipynb[notebooks/chapter1.5_removing_colinear_variables.ipynb]
• chapter2_wrapper_method_RFE_SFS.ipynb[notebooks/chapter2_wrapper_method_RFE_SFS.ipynb]
• chapter3_embedded_method.ipynb[notebooks/chapter3_embedded_method.ipynb]
• chapter4_boruta.ipynb[notebooks/chapter4_boruta.ipynb]
• chapter5_knockoffs.ipynb[notebooks/chapter5_knockoffs.ipynb]
• chapter6_stability_selection.ipynb[notebooks/chapter6_stability_selection.ipynb]
• chapter7_SHAP.ipynb[notebooks/chapter7_SHAP.ipynb]

The jupyter notebooks interleaves theory, code demo, and exercises.

Toward the end of the course, the participant will choose a project to apply what they learned:

• project_OPERA.ipynb[notebooks/project_OPERA.ipynb]
• project_Ornish.ipynb[notebooks/project_Ornish.ipynb]
• project_Xsurv.ipynb[notebooks/project_Xsurv.ipynb]