Tutorials
=========
These tutorials demonstrate integrated workflows that combine multiple BioNeuralNet components.

.. toctree::
   :maxdepth: 1

   example_1
   example_2
   example_3
   example_4


**Example 1: Sparse Multiple Canonical Correlation Network (SmCCNet) Workflow with Graph Neural Network (GNN) Embeddings**  
This tutorial demonstrates how to:

   - Construct a network using SmCCNet.
   - Generate node embeddings with a Graph Neural Network (GNN).
   - Integrate embeddings into subject-level omics data for enhanced analysis.

**Example 2: Weighted Gene Co-expression Network Analysis (WGCNA) Workflow with Graph Neural Network (GNN) Embeddings**  
This tutorial illustrates:

   - Building a weighted gene co-expression network using WGCNA.
   - Generating embeddings via a GNN and integrating them into omics data.
   - Enhancing subject-level representations for downstream analyses.

**Example 3: Disease Prediction Using Graph Information (SmCCNet + DPMON)**  
This tutorial demonstrates how to:

   - Generate an adjacency matrix using SmCCNet from multi-omics data.
   - Use DPMON for disease prediction by integrating the generated network and omics data.
   - Predict disease phenotypes leveraging graph-based models.

**Example 4: Sparse Multiple Canonical Correlation Network (SmCCNet) + PageRank Clustering + Visualization**  
This tutorial showcases:

   - Constructing a network using SmCCNet.
   - Identifying meaningful clusters in the network using PageRank-based clustering.
   - Visualizing the resulting sub-networks for biological insights.

By following these tutorials, you will learn how BioNeuralNe' modules can interoperate to:

   - Perform advanced multi-omics analyses.
   - Generate networks and embeddings.
   - Predict disease phenotypes.
   - Visualize and interpret biological graphs.