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README.md

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-
-# DeepTraCDR: Prediction Cancer Drug Response using multimodal deep learning with Transformers
-<!-- 
-## Abstract
-<div align="justify">
-
-</div> -->
-
-
-<!-- ## Method
-<img width="810" alt="image" src="https://github.com/akianfar/Deep-CBN/blob/main/assest/Artboard%202.jpg">
-<img width="810" alt="image" src="https://github.com/akianfar/Deep-CBN/blob/main/assest/Artboard%203.jpg"> -->
-
-### Requirements 
-
-To run this project, you need to install the required dependencies first. Execute the following command in your terminal or command prompt:
-
-```bash
-pip install -r requirements.txt
-```
-
-
-
-## DeepTraCDR Model Overview
-
-DeepTraCDR is a modular model consisting of **Common Modules** and **Experimental Modules**.
-
-### Common Modules
-- **Data**: Includes datasets for model training and evaluation:
-  - **GDSC**: Contains `cell_drug.csv` (log IC50 matrix), `cell_drug_binary.csv` (binary matrix), `cell_exprs.csv` (gene expression), `drug_feature.csv` (drug fingerprints), `null_mask.csv` (null values), and `threshold.csv` (sensitivity threshold).
-  - **CCLE**: Similar to GDSC with `cell_drug.csv`, `cell_drug_binary.csv`, `cell_exprs.csv`, and `drug_feature.csv`.
-  - **PDX**: Includes `pdx_response.csv` (binary patient-drug matrix), `pdx_exprs.csv` (gene expression), `pdx_null_mask.csv` (null values), and `drug_feature.csv`.
-  - **TCGA**: Contains `patient_drug_binary.csv` (binary matrix), `tcga_exprs.csv` (gene expression), `tcga_null_mask.csv` (null values), and `drug_feature.csv`.
-
-
-### Experimental Modules
-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`).
-
+
+# DeepTraCDR: Prediction Cancer Drug Response using multimodal deep learning with Transformers
+<!-- 
+## Abstract
+<div align="justify">
+
+</div> -->
+
+
+<!-- ## Method
+<img width="810" alt="image" src="https://github.com/akianfar/Deep-CBN/blob/main/assest/Artboard%202.jpg">
+<img width="810" alt="image" src="https://github.com/akianfar/Deep-CBN/blob/main/assest/Artboard%203.jpg"> -->
+
+### Requirements 
+
+To run this project, you need to install the required dependencies first. Execute the following command in your terminal or command prompt:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+
+## DeepTraCDR Model Overview
+
+DeepTraCDR is a modular model consisting of **Common Modules** and **Experimental Modules**.
+
+### Common Modules
+- **Data**: Includes datasets for model training and evaluation:
+  - **GDSC**: Contains `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).
+  - **CCLE**: Similar to GDSC with `cell_drug.csv`, `cell_drug_binary.csv`, `merged_file.csv`(gene expression), and `drug_feature.csv`.
+  - **PDX**: Includes `pdx_response.csv` (binary patient-drug matrix), `pdx_exprs.csv` (gene expression), `pdx_null_mask.csv` (null values), and `drug_feature.csv`.
+  - **TCGA**: Contains `patient_drug_binary.csv` (binary matrix), `tcga_exprs.csv` (gene expression), `tcga_null_mask.csv` (null values), and `drug_feature.csv`.
+
+
+### Experimental Modules
+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.