Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer: a Step-by-Step Tutorial

The tutorial comprises 4 Jupiter notebooks (1_Experiment_1.ipynb, 2_Experiment_2_part_1.ipynb, 3_Experiment_2_part_2.ipynb, 4_Experiment_3.ipynb) a folder Data (data_clinical_patient.csv, data_clinical_sample.csv, data_mRNA_median_all_sample_Zscores.csv), and a folder Plot where the results are saved.

A colab version notebooks are also provided. If you want to run on Colab, please use the notebook in Colab Notebook folder (codes are the same, but the uploading files' directories are different). Before running the Colab notebook, please go to your Google Drive and creat a new folder named "Data_Tutorial_BC". It is the folder where you upload data and save preprocessed data and reports. Next, upload 3 original data (data_clinical_patient.csv, data_clinical_sample.csv, data_mRNA_median_all_sample_Zscores.csv) to your Drive folder - Data_Tutorial_BC. Then, run the notebook in a sequence from 1 to 4.

Please note that you will be asked to authorise to connect data from your Drive by inputting the code. To do this, once you run the cell mounting to Drive folder, follow the link in the output session, login with your google account. Next, you copy an authorised code, paste to output session. Then, everything is good to go.