added arxiv citations

.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**

README.md

@@ -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:
@@ -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>

bioneuralnet/utils/__init__.py

@@ -4,9 +4,4 @@
 from .preprocess import preprocess_clinical, clean_inf_nan, select_top_k_variance, select_top_k_correlation, select_top_randomforest, top_anova_f_features, prune_network, prune_network_by_quantile, network_remove_low_variance, network_remove_high_zero_fraction
 from .graph import gen_similarity_graph, gen_correlation_graph, gen_threshold_graph, gen_gaussian_knn_graph, gen_lasso_graph, gen_mst_graph, gen_snn_graph
 
-
-__all__ = ["get_logger", "rdata_to_df", "variance_summary", "zero_fraction_summary", "expression_summary", "correlation_summary",
-           "explore_data_stats", "preprocess_clinical", "clean_inf_nan", "select_top_k_variance", "select_top_k_correlation",
-           "select_top_randomforest", "top_anova_f_features", "prune_network", "prune_network_by_quantile", "network_remove_low_variance",
-           "network_remove_high_zero_fraction", "gen_similarity_graph", "gen_correlation_graph", "gen_threshold_graph",
-           "gen_gaussian_knn_graph", "gen_lasso_graph", "gen_mst_graph", "gen_snn_graph"]
+__all__ = ["get_logger", "rdata_to_df", "variance_summary", "zero_fraction_summary", "expression_summary", "correlation_summary", "explore_data_stats", "preprocess_clinical", "clean_inf_nan", "select_top_k_variance", "select_top_k_correlation", "select_top_randomforest", "top_anova_f_features", "prune_network", "prune_network_by_quantile", "network_remove_low_variance", "network_remove_high_zero_fraction", "gen_similarity_graph", "gen_correlation_graph", "gen_threshold_graph", "gen_gaussian_knn_graph", "gen_lasso_graph", "gen_mst_graph", "gen_snn_graph"]

bioneuralnet/utils/graph.py

@@ -88,7 +88,7 @@ def gen_correlation_graph(X: pd.DataFrame, k: int = 15,method: str = 'pearson',
     """
     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.
+    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: