Network-based transfer learning models for biomarker selection and cancer outcome prediction

Softerware requirement

• Python 3.6 and up
• required packages:
• • Numpy
• • Pandas
• • Pickle
• • scikit-learn
• • scipy
• • random
• • warnings

How to run the project

Download eight .pk files(sample_breast_expression.pk, sample_breast_label.pk, sample_ov_expression.pk, sample_ov_label.pk, sample_PRAD.pkl, sample_PRAD_label.pkl, sample_ppi.pkl, sample_gene_names.pkl) and two .py files(NetTL_three_domains.py, NetSTL_three_domains.py) into the same fold. Run python command and execute: python3 NetTL(NetSTL)_three_domains.py The results will be exported as txt files in the same fold.

File description

• sample_OV.pkl: : Ovarian Cancer gene expression data set. The size of the data is 100 x 500, 100 samples and 500 genes.

• sample_OV_label.pkl: : Ovarian Cancer label data. The size of it is 100.

• sample_BRCA.pkl: This is a sample file of Breast Cancer gene expression set. The size of the data is 100 x 500, 100 samples and 200 genes.

• sample_BRCA_label.pkl: : Breast Cancer label data. The size of it is 100.

• sample_PRAD.pkl: This is a sample file of Prostate adenocarcinoma Cancer gene expression set. The size of the data is 100 x 500, 100 samples and 200 genes.

• sample_PRAD_label.pkl: : Prostate adenocarcinoma Cancer label data. The size of it is 100.

• sample_gene_names.pkl: 500 gene names.

• sample_ppi.pkl: Protein-protein interaction networks, dict file with 7932 keys.

• NetTL(NetSTL)_three_domains.py: execution files.