Added archival DOI

.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

@@ -121,3 +121,4 @@ dpmon_output_*
 lib/
 .DS_Store
 cancer_output_1/GlobalNetwork.csv
+Parkinson

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 +89,32 @@ 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**

README.md

@@ -6,9 +6,10 @@
 [![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.17503084.svg)](https://doi.org/10.5281/zenodo.17503084)
 
 
-## Welcome to BioNeuralNet 1.1.1
+## Welcome to BioNeuralNet 1.1.3
 
 ![BioNeuralNet Logo](assets/LOGO_WB.png)
 
@@ -31,16 +32,16 @@ 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}, 
+      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}, 
+      url={https://arxiv.org/abs/2507.20440},
 }
 ```
 </details>
@@ -105,10 +106,10 @@ 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.
 
@@ -198,7 +199,7 @@ dpmon = DPMON(
     model="GCN",
     repeat_num=5,
     tune=True,
-    gpu=True, 
+    gpu=True,
     cuda=0,
     output_dir="./output"
 )
@@ -275,24 +276,24 @@ See the [LICENSE](LICENSE) file for details.
 
 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). 
+> 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}, 
+      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}, 
+      url={https://arxiv.org/abs/2507.20440},
 }
 ```
-</details>
+</details>

bioneuralnet/__init__.py

@@ -29,7 +29,7 @@
     - `datasets`: Contains example (synthetic) datasets for testing and demonstration purposes.
 """
 
-__version__ = "1.1.1"
+__version__ = "1.1.3"
 
 from .network_embedding import GNNEmbedding
 from .downstream_task import SubjectRepresentation

bioneuralnet/utils/graph.py

@@ -10,15 +10,15 @@
 
 def gen_similarity_graph(X:pd.DataFrame, k:int = 15, metric:str = "cosine", mutual:bool = False, per_node:bool = True, self_loops:bool = False) -> pd.DataFrame:
     """
-    Build a normalized k-nearest neighbors (kNN) similarity graph from feature vectors.  
+    Build a normalized k-nearest neighbors (kNN) similarity graph from feature vectors.
 
     The function computes pairwise `cosine` or `Euclidean` distances, sparsifies the matrix by keeping `top-k` neighbours per node (or by applying a global threshold), optionally prunes edges to mutual neighbours, and can add self-loops.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - k: Number of neighbors to keep per node.
-        - metric: `cosine` or `Euclidean` (uses gaussian kernel on distances).  
+        - metric: `cosine` or `Euclidean` (uses gaussian kernel on distances).
         - mutual: If `True`, retain only mutual edges (i->j and j->i).
         - per_node: If `True`, use per-node `k`, else apply a global cutoff.
         - self_loops: If `True`, add a self-loop weight of 1.
@@ -36,7 +36,7 @@ def gen_similarity_graph(X:pd.DataFrame, k:int = 15, metric:str = "cosine", mutu
         x_torch = torch.tensor(X.values, dtype=torch.float32, device=device)
     else:
         raise TypeError("X must be a pandas.DataFrame")
-    
+
     N = x_torch.size(0)
     k = min(k, N-1)
 
@@ -80,18 +80,18 @@ def gen_similarity_graph(X:pd.DataFrame, k:int = 15, metric:str = "cosine", mutu
     if final_graph.shape != (number_of_omics, number_of_omics):
         logger.info(f"Please make sure your input X follows the description: A DataFrame (N, D) where, N(rows) is the number of subjects/samples and D(columns) represents the multi-omics features.")
         raise ValueError(f"Generated graph shape {final_graph.shape} does not match expected shape ({number_of_omics}, {number_of_omics}).")
-    
+
     return final_graph
 
 
 def gen_correlation_graph(X: pd.DataFrame, k: int = 15,method: str = 'pearson', mutual: bool = False, per_node: bool = True,threshold: Optional[float] = None, self_loops:bool = False) -> pd.DataFrame:
     """
-    Build a normalized k-nearest neighbors (kNN) correlation graph from feature vectors.  
+    Build a normalized k-nearest neighbors (kNN) correlation graph from feature vectors.
 
     The function computes pairwise `pearson` or `spearman` correlations, sparsifies the matrix by keeping `top-k` neighbours per node (or by applying a global threshold), optionally prunes edges to mutual neighbours, and can add self-loops.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - k: Number of neighbors to keep per node.
         - method: `pearson` or `spearman`.
@@ -170,12 +170,12 @@ def gen_correlation_graph(X: pd.DataFrame, k: int = 15,method: str = 'pearson',
 
 def gen_threshold_graph(X:pd.DataFrame, b: float = 6.0,k: int = 15, mutual: bool = False, self_loops: bool = False) -> pd.DataFrame:
     """
-    Build a normalized k-nearest neighbors (kNN) soft-threshold co-expression graph, similar to the network-construction step in WGCNA.  
+    Build a normalized k-nearest neighbors (kNN) soft-threshold co-expression graph, similar to the network-construction step in WGCNA.
 
     The function computes absolute pair-wise Pearson correlations, applies apower-law soft threshold with exponent `b`, sparsifies the matrix bykeeping `top-k` neighbours per node, optionally prunes edges to mutualneighbours, and can add self-loops.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - b: Soft-threshold exponent applied to absolute correlations.
         - k: Number of neighbors to keep per node.
@@ -235,12 +235,12 @@ def gen_threshold_graph(X:pd.DataFrame, b: float = 6.0,k: int = 15, mutual: bool
 
 def gen_gaussian_knn_graph(X: pd.DataFrame,k: int = 15,sigma: Optional[float] = None ,mutual: bool = False, self_loops: bool = True) -> pd.DataFrame:
     """
-    Build a normalized k-nearest neighbors (kNN) similarity graph using a Gaussian(RBF) kernel.  
+    Build a normalized k-nearest neighbors (kNN) similarity graph using a Gaussian(RBF) kernel.
 
     The function computes pairwise Euclidean distances, converts them to similarities with a Gaussian kernel (bandwidth `sigma`; if `None`, the median-distance heuristic is used), sparsifies the matrix by keeping `top-k` neighbours per node, optionally prunes edges to mutual neighbours, and can add self-loops.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - k: Number of neighbors to keep per node.
         - sigma: Bandwidth of the Gaussian kernel; if `None`, uses the median squared distance.
@@ -295,12 +295,12 @@ def gen_gaussian_knn_graph(X: pd.DataFrame,k: int = 15,sigma: Optional[float] =
 
 def gen_lasso_graph(X: pd.DataFrame, alpha: float = 0.01, self_loops: bool = False, max_iter:int = 500) -> pd.DataFrame:
     """
-    Build a sparse network using Graphical Lasso (inverse-covariance estimation).  
+    Build a sparse network using Graphical Lasso (inverse-covariance estimation).
 
     The function fits a precision matrix with L1 regularization (`alpha`),converts the non-zero entries to edge weights, can add self-loops, and row-normalizes the result.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - alpha: Regularization strength for Graphical Lasso; larger values yield sparser graphs.
         - self_loops: If `True`, add a self-loop weight of 1.
@@ -313,7 +313,7 @@ def gen_lasso_graph(X: pd.DataFrame, alpha: float = 0.01, self_loops: bool = Fal
     if isinstance(X, pd.DataFrame):
         nodes = X.columns
         number_of_omics = len(nodes)
-        x_numpy = X.values 
+        x_numpy = X.values
     else:
         raise TypeError("X must be a pandas.DataFrame")
 
@@ -344,12 +344,12 @@ def gen_lasso_graph(X: pd.DataFrame, alpha: float = 0.01, self_loops: bool = Fal
 
 def gen_mst_graph(X: pd.DataFrame, self_loops: bool = False) -> pd.DataFrame:
     """
-    Build a minimum-spanning-tree (MST) graph from feature vectors.  
+    Build a minimum-spanning-tree (MST) graph from feature vectors.
 
     The function computes pairwise Euclidean distances, extracts the MST, can add self-loops, and row-normalizes the result.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - self_loops: If `True`, add a self-loop weight of 1.
 
@@ -410,12 +410,12 @@ def gen_mst_graph(X: pd.DataFrame, self_loops: bool = False) -> pd.DataFrame:
 
 def gen_snn_graph(X: pd.DataFrame,k: int = 15,mutual: bool = False, self_loops: bool = False) -> pd.DataFrame:
     """
-    Build a shared-nearest-neighbor (SNN) graph from feature vectors.  
+    Build a shared-nearest-neighbor (SNN) graph from feature vectors.
 
     The function first finds the `top-k` nearest neighbours for each node,counts how many neighbours two nodes share, converts that count to anSNN similarity score, optionally prunes edges to mutual neighbours, and can add self-loops.
 
     Args:
-    
+
         - X: Dataframe of shape (N, D) where, N(`rows`) is the number of subjects/samples and D(`columns`) represents the multi-omics features.
         - k: Number of neighbors to keep per node.
         - mutual: If `True`, retain only mutual edges (i->j and j->i).

docs/source/Quick_Start.ipynb

@@ -896,7 +896,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BioNeuralNet version: 1.1.1\n"
+      "BioNeuralNet version: 1.1.3\n"
      ]
     }
    ],

docs/source/conf.py

@@ -7,7 +7,7 @@
 try:
     release = metadata.version("bioneuralnet")
 except metadata.PackageNotFoundError:
-    release = "1.1.1"
+    release = "1.1.3"
 
 project = "BioNeuralNet"
 version = release

docs/source/index.rst

@@ -13,6 +13,9 @@ BioNeuralNet: Graph Neural Networks for Multi-Omics Network Analysis
 .. image:: https://img.shields.io/badge/GitHub-View%20Code-blue
    :target: https://github.com/UCD-BDLab/BioNeuralNet
 
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17503084.svg
+   :target: https://doi.org/10.5281/zenodo.17503084
+
 .. figure:: _static/LOGO_TB.png
    :align: center
    :alt: BioNeuralNet Logo

requirements.txt

@@ -5,6 +5,7 @@ matplotlib
 scikit-learn
 networkx
 python-louvain
+pydantic
 ray[tune]
 statsmodels
 # torch