Tutorials

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

• Example 1: SmCCNet + GNN Embeddings + Subject Representation
• Example 2: SmCCNet + DPMON for Disease Prediction

Example 1 demonstrates:

• Generating a network adjacency using SmCCNet (external tool).

• Building GNN embeddings from the adjacency.

• Integrating embeddings into subject data for further analysis.

Example 2 demonstrates:

• Constructing a network (SmCCNet).

• Leveraging DPMON for end-to-end disease prediction, combining adjacency and omics data.

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

• Graph Embedding: Generate GNN or Node2Vec 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.

Usage Examples

• Graph Embeddings
• Subject Representation
• Disease Prediction (DPMON)
• Disease Prediction
• Graph Clustering

These examples illustrate typical usage patterns for each module.