Upload BC dataset splits and detailed data card

.gitattributes

@@ -62,3 +62,7 @@ test.csv filter=lfs diff=lfs merge=lfs -text
 train.csv filter=lfs diff=lfs merge=lfs -text
 validation.csv filter=lfs diff=lfs merge=lfs -text
 dataset.csv filter=lfs diff=lfs merge=lfs -text
+processed_data/X_test.csv filter=lfs diff=lfs merge=lfs -text
+processed_data/X_train.csv filter=lfs diff=lfs merge=lfs -text
+processed_data/X_val.csv filter=lfs diff=lfs merge=lfs -text
+processed_data/merged_dataset.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -8,126 +8,70 @@ tags:
 - healthcare
 - breast-cancer
 - gene-expression
+- bioconductor
+- genefu
+- distant-metastasis-free-survival
 - uncertainty-quantification
 - clinical-prediction
-pretty_name: Breast Cancer DMFS Gene Expression Dataset
+pretty_name: Breast Cancer Gene Expression DMFS Dataset
 size_categories:
 - n<1K
 ---
 
-# Breast Cancer DMFS Gene Expression Dataset
+# Breast Cancer Gene Expression DMFS Dataset
 
 ## Dataset Summary
 
-This dataset is derived from the Bioconductor `genefu` breast cancer gene expression resources and is formatted for binary classification of distant metastasis-free survival (DMFS) event occurrence.
-
-The dataset contains gene expression features paired with a binary clinical outcome label. It represents a small-sample, high-dimensional prediction setting, making it useful for evaluating predictive uncertainty and model robustness under limited-data conditions.
-
-## Source Data
-
-Original source:
-
-- Bioconductor `genefu` package
-- Documentation: https://www.bioconductor.org/packages//2.10/bioc/vignettes/genefu/inst/doc/genefu.pdf
-
-## Prediction Task
-
-The prediction task is binary classification.
-
-Target variable:
-
-- `dmfs_label`
-
-Label definition:
-
-- `1`: DMFS event occurred
-- `0`: No DMFS event occurred
-
-## Dataset Files
-
-This repository contains the following files:
-
-- `train.csv`: training split
-- `validation.csv`: validation split
-- `test.csv`: test split
-- `dataset.csv`: full merged dataset containing features and labels
-
-Each split file contains:
-
-- Gene expression features
-- `dmfs_label`, the binary outcome label
-
-## Dataset Statistics
-
-| Split | N | Negatives | Positives | Positive Rate |
-|---|---:|---:|---:|---:|
-| Train | 278 | 203 | 75 | 0.2698 |
-| Validation | 60 | 44 | 16 | 0.2667 |
-| Test | 60 | 43 | 17 | 0.2833 |
-| Total | 398 | 290 | 108 | 0.2714 |
-
-Additional dataset information:
-
-- Number of samples: 398
-- Number of input features: 22283
-- Feature type: continuous gene expression values
-- Target type: binary label
-
-## Data Processing Pipeline
-
-The released dataset was generated using the following preprocessing procedure:
-
-1. Loaded the cohort file containing `sample_id` and `dmfs_label`
-2. Loaded the feature matrix from `dataset.csv`
-3. Merged features and labels using `sample_id`
-4. Randomly shuffled the merged dataset using `random_state=123`
-5. Removed `sample_id` from the modeling features
-6. Split the data into train, validation, and test sets using a 70/15/15 split
-7. Stratified both split steps by `dmfs_label`
-8. Used `random_state=132` for reproducible train/validation/test splitting
-
-No feature selection, normalization, dimensionality reduction, or imputation was applied in this released version.
-
-## Intended Use
-
-This dataset is intended for research on:
-
-- Binary clinical prediction
-- Uncertainty quantification
-- Decision-aware model evaluation
-- Risk-coverage analysis
-- Small-sample, high-dimensional biomedical machine learning
-
-## Out-of-Scope Use
-
-This dataset should not be used for direct clinical decision-making, diagnosis, prognosis, or treatment selection without external validation and appropriate clinical oversight.
-
-## Limitations
-
-Important limitations include:
-
-- Small sample size
-- High-dimensional feature space
-- Potential risk of overfitting
-- Retrospective data source
-- Possible label noise in clinical outcome annotation
-- Limited generalizability without external validation
-
-## Ethical Considerations
-
-This dataset is derived from publicly available research data. It does not contain directly identifiable patient information in this release. However, because it is biomedical data, users should handle it responsibly and avoid using it for clinical deployment without further validation.
-
-## Reproducibility
-
-The train/validation/test splits were generated using fixed random seeds:
-
-- Shuffle seed: `123`
-- Split seed: `132`
-- Split ratio: 70% train, 15% validation, 15% test
-- Stratification variable: `dmfs_label`
-
-## Citation
-
-If you use this dataset, please cite the original Bioconductor `genefu` resource and the associated source publications.
-
-You may also cite this dataset repository if used as part of an uncertainty quantification benchmark.
+This dataset is derived from public breast cancer gene-expression datasets distributed through Bioconductor and used in the `genefu` breast cancer gene-expression analysis vignette. The original data were first downloaded and initially processed in R using Bioconductor packages, including `genefu`, `Biobase`, `breastCancerMAINZ`, and `breastCancerTRANSBIG`.
+
+The released Hugging Face repository is organized into two data levels:
+
+1. **Intermediate Data**: CSV files exported from the initial R/Bioconductor processing step.
+2. **Processed Data**: model-ready train, validation, and test splits generated in Python.
+
+The prediction task is binary classification of distant metastasis-free survival event status, using gene-expression features as input.
+
+## Source and Initial R Processing
+
+The true raw data are not manually downloaded as standalone CSV files. Instead, they are loaded directly in R from Bioconductor experimental data packages.
+
+The initial R script performs the following steps:
+
+- Loads the `genefu`, `Biobase`, and related packages.
+- Installs and loads Bioconductor breast cancer data packages.
+- Loads the `MAINZ` and `TRANSBIG` breast cancer cohorts.
+- Removes columns in the MAINZ phenotype table that are entirely missing.
+- Combines the MAINZ and TRANSBIG datasets using `Biobase::combine`.
+- Extracts phenotype metadata using `pData`.
+- Extracts gene-expression values using `exprs`.
+- Keeps only samples with non-missing distant metastasis-free survival time and event indicators.
+- Transposes the expression matrix so that rows correspond to samples and columns correspond to gene-expression probes.
+- Constructs a cohort table containing:
+  - `sample_id`
+  - `t_dmfs`
+  - `e_dmfs`
+  - `dmfs_label`
+
+The binary label is defined as:
+
+- `dmfs_label = 1` if `e_dmfs == 1`
+- `dmfs_label = 0` otherwise
+
+## Repository Structure
+
+```text
+BC/
+├── intermediate_data/
+│   ├── cohort.csv
+│   └── 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
+│
+└── README.md

intermediate_data/cohort.csv

@@ -0,0 +1,399 @@
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