Update branch gnn-explainability with branch main

.github/workflows/publish.yml

@@ -1,8 +1,9 @@
-name: Publish to PyPI and GitHub Packages
+name: Publish to PyPI
+
 on:
-  push:
-    tags:
-      - "v*.*.*"
+  release:
+    types: [published]
+
 jobs:
   build-and-publish:
     runs-on: ubuntu-latest
@@ -12,32 +13,21 @@ jobs:
 
       - name: Set up Python
         uses: actions/setup-python@v4
-
         with:
-          python-version: "3.10"
-      - name: Install build tools
+          python-version: "3.12"
 
+      - name: Install build tools
         run: |
           python -m pip install --upgrade pip setuptools wheel twine
+          python -m pip install build
 
       - name: Build source & wheel
         run: |
-          python setup.py sdist bdist_wheel
-
-#      - name: Publish to GitHub Packages
-#        env:
-#          TWINE_USERNAME: __token__
-#          TWINE_PASSWORD: ${{ secrets.GITHUB_TOKEN }}
-#        run: |
-#          python -m pip install --upgrade pip twine
-#          python -m twine upload \
-#            --repository-url https://api.github.com/orgs/${{ github.repository_owner }}/packages/pypi/upload \
-#            dist/*
+          python -m build
 
       - name: Publish to PyPI
         env:
           TWINE_USERNAME: __token__
           TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
-
         run: |
           twine upload dist/*

.gitignore

@@ -28,6 +28,7 @@ TCGA-BRCA_Dataset_testing*.ipynb
 testsOLD
 network_construction_run.ipynb
 datasets_run.ipynb
+jupyter_execute
 # Other example data and tests not needed in the repo.
 
 Output**
@@ -68,8 +69,13 @@ TCGA_BRCA_DATA/
 !bioneuralnet/datasets/brca/
 !bioneuralnet/datasets/brca/**/*.csv
 
-!bioneuralnet/datasets/networks/
-!bioneuralnet/datasets/networks/**/*.csv
+!bioneuralnet/datasets/kipan/
+!bioneuralnet/datasets/kipan/**/*.csv
+
+!bioneuralnet/datasets/gbmlgg/
+!bioneuralnet/datasets/gbmlgg/**/*.csv
+
+
 feature_testing
 
 # Sphinx documentation build
@@ -120,3 +126,13 @@ dpmon_output_*
 lib/
 .DS_Store
 cancer_output_1/GlobalNetwork.csv
+Parkinson
+KIPAN
+dpmon_cv_results
+dpmon_cv_results_GCN_FINAL
+dpmon_cv_results_SAGE_FINAL
+dpmon_tuning
+GBMLGG
+PAAN
+dpmon_cv_results_GAT_FINAL
+docs_notebooks

CHANGELOG.md

@@ -70,16 +70,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/).
 
 ### **Added**
 - **New Embedding Integration Utility**
-  - `_integrate_embeddings(reduced, method="multiply", alpha=2.0, beta=0.5)`: 
-    - Integrates reduced embeddings with raw omics features via a multiplicative scheme:  
-    - `enhanced = beta * raw + (1 - beta) * (alpha * normalized_weight * raw)`  
+  - `_integrate_embeddings(reduced, method="multiply", alpha=2.0, beta=0.5)`:
+    - Integrates reduced embeddings with raw omics features via a multiplicative scheme:
+    - `enhanced = beta * raw + (1 - beta) * (alpha * normalized_weight * raw)`
     - (default ensures ≥ 50 % of each feature’s final value is influenced by the learned weights).
 
 - **Graph-Generation Algorithms**
-  - `gen_similarity_graph`: k-NN Cosine / Gaussian RBF similarity graph  
-  - `gen_correlation_graph`: Pearson / Spearman co-expression graph  
-  - `gen_threshold_graph`: soft-threshold (WGCNA-style) correlation graph  
-  - `gen_gaussian_knn_graph`: Gaussian kernel k-NN graph  
+  - `gen_similarity_graph`: k-NN Cosine / Gaussian RBF similarity graph
+  - `gen_correlation_graph`: Pearson / Spearman co-expression graph
+  - `gen_threshold_graph`: soft-threshold (WGCNA-style) correlation graph
+  - `gen_gaussian_knn_graph`: Gaussian kernel k-NN graph
   - `gen_mutual_info_graph`: mutual-information graph
 
 - **Preprocessing Utilities**
@@ -89,24 +89,61 @@ and this project adheres to [Semantic Versioning](https://semver.org/).
   - Correlation selection (supervised / unsupervised): `select_top_k_correlation`
   - RandomForest importance: `select_top_randomforest`
   - ANOVA F-test selection: `top_anova_f_features`
-  - Network-pruning helpers:  
-      - `prune_network`, `prune_network_by_quantile`,  
+  - Network-pruning helpers:
+      - `prune_network`, `prune_network_by_quantile`,
       - `network_remove_low_variance`, `network_remove_high_zero_fraction`
 
-- **Continuous-Deployment Workflow**  
+- **Continuous-Deployment Workflow**
   Added `.github/workflows/publish.yml` to auto-publish releases to PyPI when a Git tag is pushed.
 
-- **Updated Homepage Image**  
+- **Updated Homepage Image**
   Replaced the index-page illustration to depict the full BioNeuralNet workflow.
 
 ### **Changed**
 - **Comprehensive Documentation Update**
-  - Rebuilt ReadTheDocs site with a new workflow diagram on the landing page.  
-  - Synced API reference to include all new graph-generation, preprocessing, and embedding-integration functions.  
-  - Added quick-start guide, expanded tutorials, and refreshed examples/notebooks.  
+  - Rebuilt ReadTheDocs site with a new workflow diagram on the landing page.
+  - Synced API reference to include all new graph-generation, preprocessing, and embedding-integration functions.
+  - Added quick-start guide, expanded tutorials, and refreshed examples/notebooks.
   - Updated narrative docs, docstrings, and licencing info for consistency.
 
 - **License**: Project is now distributed under the [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
 
 ### **Fixed**
 - **Packaging Bug**: Missing `.csv` datasets and `.R` scripts in source distribution; `MANIFEST.in` updated to include all requisite data files.
+
+## [1.1.2] - 2025-11-01
+
+- **Linked Zenodo DOI to GitHub repository**
+
+## [1.1.3] - 2025-11-01
+
+- **Tag update to Sync Zenodo and PIPY**
+
+## [1.1.4] - 2025-11-08
+
+### **Added**
+- **New Utility Functions and Global Seed**
+
+  - **Imputation**:
+    - `impute_omics`: Imputes missing values (NaNs) using `mean`, `median`, or `zero` strategies.
+
+    - `impute_omics_knn`: Implements **K-Nearest Neighbors (KNN)** imputation for missing values in omics data.
+
+  - **Normalization/Scaling**:
+    - `normalize_omics`: Scales feature data using `Z-score`, `MinMax` scaling, or `Log2` transformation.
+
+  - **Methylation Data Transformation**:
+      - `beta_to_m`: Converts methylation `Beta-values to M-values` (log2 transformation).
+
+  - **Reproducibility Utility**:
+    - `set_seed`: Sets global random seeds across Python, NumPy, and PyTorch, including configuration for deterministic CUDA operations to ensure maximum experimental reproducibility.
+
+- **New TCGA Datasets**
+
+  - **TCGA-KIPAN**: Added the Pan-Kidney cohort (KIRC, KIRP, KICH) to **DatasetLoader**.
+
+  - **TCGA-GBMLGG**: Added the combined Glioblastoma Multiforme and Lower-Grade Glioma cohort to **DatasetLoader**.
+
+### **Changed**
+
+  - **Documentation Update**: Updated the online documentation (Read the Docs/API Reference) to include the new TCGA datasets and their respective classification results using the **DPMON**.

MANIFEST.in

@@ -14,12 +14,8 @@ recursive-include bioneuralnet/utils *.R *.r
 recursive-include bioneuralnet/datasets/monet *.csv
 recursive-include bioneuralnet/datasets/example1 *.csv
 recursive-include bioneuralnet/datasets/brca *.csv
-
-# include bioneuralnet/datasets/brca/clinical.csv
-# include bioneuralnet/datasets/brca/pam50.csv
-# include bioneuralnet/datasets/brca/mirna.csv
-# exclude bioneuralnet/datasets/brca/meth_*.csv
-# exclude bioneuralnet/datasets/brca/rna_*.csv
+recursive-include bioneuralnet/datasets/gbmlgg *.csv
+recursive-include bioneuralnet/datasets/kipan *.csv
 
 # Include documentation source files
 prune docs

README.md

@@ -6,9 +6,9 @@
 [![GitHub Contributors](https://img.shields.io/github/contributors/UCD-BDLab/BioNeuralNet)](https://github.com/UCD-BDLab/BioNeuralNet/graphs/contributors)
 [![Downloads](https://static.pepy.tech/badge/bioneuralnet)](https://pepy.tech/project/bioneuralnet)
 [![Documentation](https://img.shields.io/badge/docs-read%20the%20docs-blue.svg)](https://bioneuralnet.readthedocs.io/en/latest/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17503083.svg)](https://doi.org/10.5281/zenodo.17503083)
 
-
-## Welcome to BioNeuralNet 1.1.1
+## Welcome to BioNeuralNet 1.1.4
 
 ![BioNeuralNet Logo](assets/LOGO_WB.png)
 
@@ -17,9 +17,38 @@
 
 ![BioNeuralNet Workflow](assets/BioNeuralNet.png)
 
+
+## Citation
+
+If you use BioNeuralNet in your research, we kindly ask that you cite our paper:
+
+> Ramos, V., Hussein, S., et al. (2025).
+> [**BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool**](https://arxiv.org/abs/2507.20440).
+> *arXiv preprint arXiv:2507.20440* | [**DOI: 10.48550/arXiv.2507.20440**](https://doi.org/10.48550/arXiv.2507.20440).
+
+
+For your convenience, you can use the following BibTeX entry:
+
+<details>
+  <summary>BibTeX Citation</summary>
+
+```bibtex
+@misc{ramos2025bioneuralnetgraphneuralnetwork,
+      title={BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool},
+      author={Vicente Ramos and Sundous Hussein and Mohamed Abdel-Hafiz and Arunangshu Sarkar and Weixuan Liu and Katerina J. Kechris and Russell P. Bowler and Leslie Lange and Farnoush Banaei-Kashani},
+      year={2025},
+      eprint={2507.20440},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2507.20440},
+}
+```
+</details>
+
 ## Documentation
 
-**[BioNeuralNet Documentation & Examples](https://bioneuralnet.readthedocs.io/en/latest/)**
+For complete documentation, tutorials, and examples, please visit our Read the Docs site:
+**[bioneuralnet.readthedocs.io](https://bioneuralnet.readthedocs.io/en/latest/)**
 
 ## Table of Contents
 
@@ -38,12 +67,14 @@
 
 ## 1. Installation
 
-BioNeuralNet supports Python `3.10`, `3.11` and `3.12`.
+BioNeuralNet is available as a package on the Python Package Index (PyPI), making it easy to install and integrate into your workflows.
 
 ### 1.1. Install BioNeuralNet
 ```bash
 pip install bioneuralnet
 ```
+**PyPI Project Page:** [https://pypi.org/project/bioneuralnet/](https://pypi.org/project/bioneuralnet/)
+>**Requirements:** BioNeuralNet is tested and supported on Python versions `3.10`, `3.11`, and `3.12`. Functionality on other versions is not guaranteed.
 
 ## 1.2. Install PyTorch and PyTorch Geometric
 BioNeuralNet relies on PyTorch for GNN computations. Install PyTorch separately:
@@ -74,10 +105,10 @@ BioNeuralNet is a flexible, modular Python framework developed to facilitate end
   - **Similarity graphs:** k-NN (cosine/Euclidean), RBF, mutual information.
 
   - **Correlation graphs:** Pearson, Spearman; optional soft-thresholding.
-  
+
   - **Phenotype-aware graphs:** SmCCNet integration (R) for sparse multiple canonical-correlation networks.
 
-- **[Preprocessing Utilities](https://bioneuralnet.readthedocs.io/en/latest/utils.html#graph-generation):** 
+- **[Preprocessing Utilities](https://bioneuralnet.readthedocs.io/en/latest/utils.html#graph-generation):**
 
   - **RData conversion to pandas DataFrame:** Converts an RData file to CSV and loads it into a pandas DataFrame.
 
@@ -167,7 +198,7 @@ dpmon = DPMON(
     model="GCN",
     repeat_num=5,
     tune=True,
-    gpu=True, 
+    gpu=True,
     cuda=0,
     output_dir="./output"
 )
@@ -238,3 +269,30 @@ See the [LICENSE](LICENSE) file for details.
 <a id="2">[2]</a> Hussein, S., Ramos, V., et al. "Learning from Multi-Omics Networks to Enhance Disease Prediction: An Optimized Network Embedding and Fusion Approach." In *2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)*, Lisbon, Portugal, 2024, pp. 4371-4378. [DOI: 10.1109/BIBM62325.2024.10822233](https://doi.org/10.1109/BIBM62325.2024.10822233)
 
 <a id="3">[3]</a> Liu, W., Vu, T., Konigsberg, I. R., Pratte, K. A., Zhuang, Y., & Kechris, K. J. (2023). "Network-Based Integration of Multi-Omics Data for Biomarker Discovery and Phenotype Prediction." *Bioinformatics*, 39(5), btat204. [DOI: 10.1093/bioinformatics/btat204](https://doi.org/10.1093/bioinformatics/btat204)
+
+
+## 11. Citation
+
+If you use BioNeuralNet in your research, we kindly ask that you cite our paper:
+
+> Vicente Ramos, et al. (2025).
+> [**BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool**](https://arxiv.org/abs/2507.20440).
+> *arXiv preprint arXiv:2507.20440*.
+
+For your convenience, you can use the following BibTeX entry:
+
+<details>
+  <summary>BibTeX Citation</summary>
+
+```bibtex
+@misc{ramos2025bioneuralnetgraphneuralnetwork,
+      title={BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool},
+      author={Vicente Ramos and Sundous Hussein and Mohamed Abdel-Hafiz and Arunangshu Sarkar and Weixuan Liu and Katerina J. Kechris and Russell P. Bowler and Leslie Lange and Farnoush Banaei-Kashani},
+      year={2025},
+      eprint={2507.20440},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2507.20440},
+}
+```
+</details>