In this repository, our models developed in the paper, "Prediction of deep molecular response in chronic myeloid leukemia using supervised machine learning models" are provided as on-line modules.
To use the modules, please go to the folder named "CML_example". Download the folder associated with the model you are interested in. Run the jupyter notebook file "run_example.ipynb", and you will be asked to enter a subject's data. Finally, the predicted probability of DMR achievement calculated by the models will be shown.
If you use the modules, please cite the paper:
@article{
Zahra Zad, Simone Bonecker, Taiyao Wang, Ilana Zalcberg, Gustavo T. Stelzer, Bruna Sabioni, Luciana Mayumi Gutiyama, Julia L. Fleck, Ioannis Ch. Paschalidis,
Prediction of deep molecular response in chronic myeloid leukemia using supervised machine learning models,
Leukemia Research,
Volume 141,
2024,
107502,
ISSN 0145-2126,
https://doi.org/10.1016/j.leukres.2024.107502.
(https://www.sciencedirect.com/science/article/pii/S0145212624000687)
Keywords: Chronic Myeloid Leukemia (CML); Imatinib (IM); Deep Molecular Response (DMR); Treatment-free remission (TFR); Supervised Machine Learning
}
Email: Zahra Zad [email protected]