Upload BC dataset splits and detailed data card

README.md

@@ -42,9 +42,7 @@ The prediction task is binary classification of distant metastasis-free survival
 
 This dataset is derived from publicly available breast cancer gene-expression cohorts distributed via Bioconductor and accessed using the `genefu` framework.
 
-For full details on the original data sources and preprocessing pipeline, refer to:
-
-- https://www.bioconductor.org/packages/devel/bioc/vignettes/genefu/inst/doc/genefu.html
+For full details on the original data sources and preprocessing pipeline, refer to the genefu website provided above.
 
 The underlying cohorts include datasets such as:
 
@@ -66,7 +64,7 @@ Key steps include:
 - Loading required libraries:
   - `genefu`
   - `Biobase`
-  - Bioconductor breast cancer datasets (e.g., MAINZ, TRANSBIG)
+  - Bioconductor breast cancer datasets
 - Loading datasets:
   - `breastCancerMAINZ`
   - `breastCancerTRANSBIG`
@@ -76,11 +74,10 @@ Key steps include:
   - Phenotype data via `pData`
   - Gene-expression data via `exprs`
 - Filtering samples with available DMFS information:
-  - Non-missing `t.dmfs` (time)
-  - Non-missing `e.dmfs` (event)
-- Constructing feature matrix:
-  - Transpose expression matrix so rows correspond to samples
-- Constructing cohort table with:
+  - Non-missing `t.dmfs`
+  - Non-missing `e.dmfs`
+- Transposing the expression matrix so rows correspond to samples
+- Constructing a cohort table with:
   - `sample_id`
   - `t_dmfs`
   - `e_dmfs`
@@ -90,7 +87,7 @@ Key steps include:
 
 The binary classification label is defined as:
 
-- `dmfs_label = 1` if `e_dmfs == 1` (event occurred)
+- `dmfs_label = 1` if `e_dmfs == 1`
 - `dmfs_label = 0` otherwise
 
 ---
@@ -104,12 +101,8 @@ BC/
 │   └── dataset.csv
 │
 ├── processed_data/
-│   ├── X_train.csv
-│   ├── X_val.csv
-│   ├── X_test.csv
-│   ├── y_train.csv
-│   ├── y_val.csv
-│   ├── y_test.csv
-│   └── merged_dataset.csv
+│   ├── train.csv
+│   ├── validation.csv
+│   └── test.csv
 │
 └── README.md

processed_data/merged_dataset.csv

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processed_data/{X_test.csv → test.csv}

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processed_data/y_test.csv

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processed_data/y_val.csv

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