More Examples
=============

These tutorials illustrate how to combine BioNeuralNet components for a cohesive multi-omics analysis.

.. toctree::
   :maxdepth: 1

   example_1
   example_2

**Example 1** demonstrates:
   - Constructing a network (SmCCNet).
   - Leveraging **DPMON** for end-to-end **disease prediction**.

**Example 2** demonstrates:
   - Generating a graph using cosine similarity.
   - Using **GNNEmbedding** to create node embeddings.
   - Integrating embeddings into subject data for further analysis.

BioNeuralNet offers a variety of **tools** for graph-based analyses of multi-omics data, including:

- **Graph Embedding**: Generate GNN embeddings.
- **Subject Representation**: Integrate embeddings into omics data..
- **Disease Prediction**: DPMON for end-to-end classification.
- **Graph Clustering**: PageRank or Hierarchical clustering for subnetwork identification.