Welcome to Feature-scML documentation!
Install
# create environment
conda create -n env -f environment.yml
# or
conda env create -f environment.yml
# activate environment
conda activate Feature_scML
#Install Feature-scML
pip install git+https://github.com/liameihao/Feature-scML.git@main -U
# or
git clone https://github.com/liameihao/Feature-scML.git
cd Feature-scML
python setup.py install
Documentation
Feature Selection – Feature ranking.
Machine learning – Training Machine Learning models.
Auto Machine Learning – Auto Machine learning training.
Plot – Plot picture.
Data Format Requirement
The data format requires csv format. The first column is the sample label (Pay attention to the case of the Label.
Label |
feature 1 |
feature 2 |
… |
1 |
2.0 |
3.0 |
… |
2 |
2.0 |
3.0 |
… |
1 |
50.0 |
80.0 |
… |
3 |
30.1 |
40.56 |
… |
Reference
F-score: Chen, Yi-Wei, and Chih-Jen Lin. “Combining SVMs with various feature selection strategies.” Feature extraction. Springer, Berlin, Heidelberg, 2006. 315-324.
CV2: Brennecke, Philip, et al. “Accounting for technical noise in single-cell RNA-seq experiments.” Nature methods 10.11 (2013): 1093-1095.
MIC: Albanese, Davide, et al. “Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers.” Bioinformatics 29.3 (2013): 407-408.
TuRF: Urbanowicz, Ryan J., et al. “Benchmarking relief-based feature selection methods for bioinformatics data mining.” Journal of biomedical informatics 85 (2018): 168-188.
shap: Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions.” Proceedings of the 31st international conference on neural information processing systems. 2017.
Other methods : Pedregosa, Fabian, et al. “Scikit-learn: Machine learning in Python.” the Journal of machine Learning research 12 (2011): 2825-2830.