|
11 hours ago | |
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Data | 5 days ago | |
Scenario1 | 3 days ago | |
Scenario2 | 11 hours ago | |
Scenario3/External | 5 days ago | |
case_study | 5 days ago | |
main/assest | 5 days ago | |
README.md | 5 days ago | |
data_loader.py | 5 days ago | |
data_sampler.py | 11 hours ago | |
requirements.txt | 5 days ago | |
utils.py | 3 days ago |
To run this project, you need to install the required dependencies first. Execute the following command in your terminal or command prompt:
pip install -r requirements.txt
DeepTraCDR is a modular model consisting of Common Modules and Experimental Modules.
cell_drug.csv
(log IC50 matrix), cell_drug_binary.csv
(binary matrix), merged_file_GDSC.csv
(gene expression), drug_feature.csv
(drug fingerprints), null_mask.csv
(null values), and threshold.csv
(sensitivity threshold).cell_drug.csv
, cell_drug_binary.csv
, merged_file.csv
(gene expression), and drug_feature.csv
.pdx_response.csv
(binary patient-drug matrix), pdx_exprs.csv
(gene expression), pdx_null_mask.csv
(null values), and drug_feature.csv
.patient_drug_binary.csv
(binary matrix), tcga_exprs.csv
(gene expression), tcga_null_mask.csv
(null values), and drug_feature.csv
.The experimental modules are organized into the following directories, each containing a main.py
script to run the respective experiment:
case_study
: Contains scripts for case study experiments (e.g., main_case_study.py
).Scenario1
: Includes experiments for random clearing cross-validation (Random
) and regression (Regression
).Scenario2
: Includes experiments for single row/column clearing (new
) and targeted drug experiments (Target
).Scenario3
: Includes external validation experiments from in vitro to in vivo (External
).Each main.py
script outputs true and predicted test data values after multiple cross-validations. The utils.py
file supports performance analysis with metrics like AUC, AUPRC, ACC, F1, and MCC. The model is built using PyTorch with CUDA support.