UCD-BDLab · abdelhafizm · Jul 28, 2025 · Jul 27, 2025
diff --git a/README.md b/README.md
diff --git a/assets/BioNeuralNet.png b/assets/BioNeuralNet.png
diff --git a/docs/source/_static/BioNeuralNet_old1.png → assets/BioNeuralNet2.png b/docs/source/_static/BioNeuralNet_old1.png → assets/BioNeuralNet2.png
diff --git a/assets/BioNeuralNet_old.png b/assets/BioNeuralNet_old.png
diff --git a/docs/source/_static/BioNeuralNet.png b/docs/source/_static/BioNeuralNet.png
diff --git a/docs/source/_static/BioNeuralNet_old.png b/docs/source/_static/BioNeuralNet_old.png
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -47,7 +47,8 @@
 html_static_path = ["_static"]
 html_css_files = ["custom.css"]
 html_theme = "furo"
-html_title = "BioNeuralNet Documentation"
+#html_title = ""
+html_logo = "_static/LOGO_TB.svg"
 #html_theme = "sphinx_rtd_theme"
 
 intersphinx_mapping = {

diff --git a/docs/source/faq.rst b/docs/source/faq.rst
@@ -31,14 +31,33 @@ Contributions to BioNeuralNet are welcome. If you wish to contribute new feature
 
 `UCD-BDLab/BioNeuralNet <https://github.com/UCD-BDLab/BioNeuralNet>`_
 
-Please refer to our contribution guidelines in the repository for more details.
+   - **Ways to contribute**:
+
+      - Report issues or bugs on our `GitHub Issues page <https://github.com/UCD-BDLab/BioNeuralNet/issues>`_.
+      - Suggest new features or improvements.
+      - Share your experiences or use cases with the community.
+
+   - **Implementing new features**:
+
+      - Fork the repo and create a feature branch.
+      - Add tests and documentation for new features.
+      - Run the test suite and and pre-commit hooks before opening a Pull Request(PR).
+      - A new PR should pass all tests and adhere to the project's coding standards.
+
+.. code-block:: bash
+
+   git clone https://github.com/UCD-BDLab/BioNeuralNet.git
+   cd BioNeuralNet
+   pip install -r requirements-dev.txt
+   pre-commit install
+   pytest --cov=bioneuralnet
 
 Frequently Asked Questions (FAQ)
 --------------------------------
 
 **Q1: What is BioNeuralNet?**:
 
-   - BioNeuralNet is a **flexible, modular Python framework** developed to facilitate end-to-end **network-based multi-omics analysis** using **Graph Neural Networks (GNNs)**. It addresses the complexities associated with multi-omics data—such as high dimensionality, sparsity, and intricate molecular interactions—by converting biological networks into meaningful, low-dimensional embeddings suitable for downstream tasks.
+   - BioNeuralNet is a flexible, modular Python framework developed to facilitate end-to-end network-based multi-omics analysis** using **Graph Neural Networks (GNNs)**. It addresses the complexities associated with multi-omics data, such as high dimensionality, sparsity, and intricate molecular interactions, by converting biological networks into meaningful, low-dimensional embeddings suitable for downstream tasks.
 
 **Q2: What are the key features of BioNeuralNet?**:
 
@@ -49,8 +68,6 @@ Frequently Asked Questions (FAQ)
 
 **Q3: How do I install BioNeuralNet?**:
 
-   - Install via pip
-
 .. code-block:: bash
 
    pip install bioneuralnet
@@ -62,41 +79,18 @@ Frequently Asked Questions (FAQ)
 
 **Q5: Can I use my own network instead of SmCCNet or internal graph generation functions?**
 
-   - Absolutely. You can supply a pre-computed adjacency matrix directly to the GNNEmbedding or DPMON modules.
-
-**Q6: How is DPMON different from standard GNN models?**  
-
-   - DPMON is tailored for multi-omics disease prediction by jointly learning node embeddings and a classifier, integrating both local and global graph structures.
-
-**Q7: What clustering methods does BioNeuralNet support?**:
-
-   - Correlated Louvain  
-   - Hybrid Louvain  
-   - Correlated PageRank
-
-**Q8: How can I contribute to BioNeuralNet?**:
-
-   - Contributions are welcome! You can:
-
-      - Report issues or bugs on our `GitHub Issues page <https://github.com/UCD-BDLab/BioNeuralNet/issues>`_.
-      - Suggest new features or improvements.
-      - Share your experiences or use cases with the community.
-
-   - How to contribute:
-
-      - Fork the repository, add your features, components, or algorithms, and submit a pull request.
-      - Please refer to our `contribution guidelines <https://github.com/UCD-BDLab/BioNeuralNet?tab=readme-ov-file#8-contributing>`_ for more details.
+   - Absolutely. You can supply a pre-computed adjacency matrix directly to the **GNNEmbedding** or **DPMON** modules.
 
-**Q9: Where can I find tutorials and examples?**:
+**Q6: Where can I find tutorials and examples?**:
 
    - We provide a set of tutorials and example notebooks to help you get started with BioNeuralNet. You can find them in the `tutorials` directory of the repository.  
    - For a quick start, check out the following notebooks:
 
       - :doc:`Quick_Start`.
       - :doc:`TCGA-BRCA_Dataset`.
 
-**Q10: What license is BioNeuralNet released under?**:
+**Q7: What license is BioNeuralNet released under?**:
 
-   - BioNeuralNet is 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/).
+   - BioNeuralNet is 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/>`_.
 
 Return to :doc:`../index`
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -13,7 +13,7 @@ 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
 
-.. figure:: _static/LOGO_WB.png
+.. figure:: _static/LOGO_TB.png
    :align: center
    :alt: BioNeuralNet Logo
 
@@ -44,21 +44,20 @@ Get started quickly with these end-to-end examples demonstrating the BioNeuralNe
    :align: center
    :alt: BioNeuralNet Workflow Overview
 
-   Embeddings form the core of BioNeuralNet, supporting diverse downstream applications.
-   `View full-size image: Network-Based Multi-Omics Analysis for Disease Prediction <https://bioneuralnet.readthedocs.io/en/latest/_images/BioNeuralNet.png>`_
+   `View BioNeuralNet Workflow. <https://bioneuralnet.readthedocs.io/en/latest/_images/BioNeuralNet.png>`_
 
 What is BioNeuralNet?
 ---------------------
 
-BioNeuralNet is a **flexible, modular Python framework** developed to facilitate end-to-end **network-based multi-omics analysis** using **Graph Neural Networks (GNNs)**. It addresses the complexities associated with multi-omics data—such as high dimensionality, sparsity, and intricate molecular interactions—by converting biological networks into meaningful, low-dimensional embeddings suitable for downstream tasks.
+BioNeuralNet is a flexible, modular Python framework developed to facilitate end-to-end network-based multi-omics analysis using **Graph Neural Networks (GNNs)**. It addresses the complexities associated with multi-omics data, such as high dimensionality, sparsity, and intricate molecular interactions, by converting biological networks into meaningful, low-dimensional embeddings suitable for downstream tasks.
 
 BioNeuralNet provides:
 
 - **Network Construction**: Easily build informative networks from multi-omics datasets to capture biologically relevant molecular interactions.
 - **GNN Embeddings**: Transform complex biological networks into versatile embeddings, capturing both structural relationships and molecular interactions.
 - **Phenotype-Aware Analysis**: Integrate phenotype or clinical variables to enhance the biological relevance of the embeddings.
 - **Disease Prediction**: Utilize network-derived embeddings for accurate and scalable predictive modeling of diseases and phenotypes.
-- **Interoperability**: Outputs structured as **pandas DataFrames**, ensuring compatibility with common Python tools and seamless integration into existing bioinformatics pipelines.
+- **Interoperability**: Outputs structured as **Pandas DataFrames**, ensuring compatibility with common Python tools and seamless integration into existing bioinformatics pipelines.
 
 BioNeuralNet emphasizes usability, reproducibility, and adaptability, making advanced network-based multi-omics analyses accessible to researchers working in precision medicine and systems biology.
 
@@ -162,6 +161,33 @@ For detailed examples and tutorials, visit:
 - :doc:`metrics`: Methods for visualization, quality evaluation, and performance benchmarking.
 - :doc:`utils`: Tools for preprocessing, feature selection, network construction, and data summarization.
 - :doc:`external_tools/index`: Integration of external methods, such as SmCCNet, for advanced network construction.
+- :doc:`user_api`: Detailed API documentation for developers and advanced users.
+
+Contributing to BioNeuralNet
+----------------------------
+
+We welcome contributions to BioNeuralNet! If you have ideas for new features, improvements, or bug fixes, please follow these steps:
+
+- **Ways to contribute**:
+
+   - Report issues or bugs on our `GitHub Issues page <https://github.com/UCD-BDLab/BioNeuralNet/issues>`_.
+   - Suggest new features or improvements.
+   - Share your experiences or use cases with the community.
+
+- **Implementing new features**:
+
+   - Fork the repo and create a feature branch `UCD-BDLab/BioNeuralNet <https://github.com/UCD-BDLab/BioNeuralNet>`_.
+   - Add tests and documentation for new features.
+   - Run the test suite and and pre-commit hooks before opening a Pull Request(PR).
+   - A new PR should pass all tests and adhere to the project's coding standards.
+
+.. code-block:: bash
+
+   git clone https://github.com/UCD-BDLab/BioNeuralNet.git
+   cd BioNeuralNet
+   pip install -r requirements-dev.txt
+   pre-commit install
+   pytest --cov=bioneuralnet
 
 
 .. toctree::