Source code of the paper:
Prediction of Early-Stage Melanoma Recurrence Using Clinical and Histopathologic Features
by Guihong Wan, Nga Nguyen, Feng Liu, Mia S. DeSimone, Bonnie W. Leung, ..., Peter K. Sorger, Kun-Hsing Yu, and Yevgeniy R. Semenov.
Accepted in principle in NPJ Precision Oncology, 2022.
Research Use Only.
data: includes an sample dataset.
melanoma_example_v1.0.csv: 30 artificial samples created to demonstrate the running of the codes.
code: includes codes for the analyses.
Melanoma_cohort_v1.0.Rmd for data preprocessing.
ml-analysis-site-binary-classification-v1.0.ipynb: binary recurrence classification tasks.
ml-analysis-site-binary-feature-importance-v1.0.ipynb: permutation feature importance in binary classification.
ml-analysis-site-time2event-prediction-v1.0.ipynb: time-to-event prediction, permutation feature importance, and sample predictions.
Data collection and analyses were performed in R 4.2.1, Python 3.8.12, NumPy 1.20.2, scikit-learn 0.24.1, and scikit-survival 0.17.2.
Please contact gwan@mgh.harvard.edu or ysemenov@mgh.harvard.edu in case you have any questions.
Please cite our paper if you use the code in your own work:
@article{earlymelanoma2022,
title={Prediction of Early-Stage Melanoma Recurrence Using Clinical and Histopathologic Features},
author={Wan, Guihong
and Nguyen, Nga
and Liu, Feng
and DeSimone, Mia S
and Leung, Bonnie W
and others},
journal={NPJ Precision Oncology},
year="2022"
}