Welcome to BioNeuralNet Beta 0.2

BioNeuralNet – Advanced Multi-Omics Integration with GNNs

Note

This is a BETA version of BioNeuralNet. It is under active development, and certain features may be incomplete or subject to change. Your feedback and bug reports are highly encouraged to help us refine and improve the tool.

Installation

To install BioNeuralNet, simply run:

pip install bioneuralnet

For additional installation details, including GPU acceleration for GNNs, see Installation.

What is BioNeuralNet?

BioNeuralNet is a Python-based framework designed to bridge the gap between multi-omics data analysis and Graph Neural Networks (GNNs). By leveraging advanced techniques, it enables:

• Graph Clustering – Identify biologically meaningful communities within omics networks.

• GNN Embeddings – Learn network-based feature representations from biological graphs.

• Subject Representation – Generate high-quality embeddings for individuals based on multi-omics profiles.

• Disease Prediction – Build predictive models using integrated multi-layer biological networks.

Seamless Data Integration

One of BioNeuralNet’s core strengths is interoperability:

• Outputs are structured as pandas DataFrames, allowing easy downstream analysis.

• Supports integration with external tools, making it adaptable to various research workflows.

• Works effortlessly with network-based and machine learning pipelines.

Designed for Researchers. Built for Innovation.

Start exploring BioNeuralNet today and push the boundaries of multi-omics analysis!

pip install bioneuralnet

For additional installation details, including GPU usage for GNNs, see Installation.

Note

External Tools:

• BioNeuralNet offers external tools available through the bioneuralnet.external_tools module.

• These lightweight wrappers (e.g., for WGCNA, SmCCNet, Node2Vec) facilitate testing and integration, but they are not mandatory to use the core functionality.

• We highly encourage users to explore these tools to fully leverage their capabilities.

Example: Transforming Multi-Omics for Enhanced Disease Prediction

View full-size image: Transforming Multi-Omics for Enhanced Disease Prediction

BioNeuralNet: Transforming Multi-Omics for Enhanced Disease Prediction

Below is a quick example demonstrating the following steps:

1. Data Preparation:

   • Input your multi-omics data (e.g., proteomics, metabolomics) along with phenotype and clinical data.

2. Network Construction:

   • Not performed internally: Generate the network adjacency matrix externally (e.g., using SmCCNet).

   • Lightweight wrappers (e.g., WGCNA, SmCCNet) are available in bioneuralnet.external_tools for convenience.

3. Disease Prediction:

   • Use DPMON to predict disease phenotypes by integrating the network information with omics data.

   • DPMON supports an end-to-end pipeline with hyperparameter tuning that can return predictions as pandas DataFrames, enabling seamless integration with existing workflows.

Code Example:

import pandas as pd
from bioneuralnet.external_tools import SmCCNet
from bioneuralnet.downstream_task import DPMON

# Step 1: Data Preparation
phenotype_data = pd.read_csv('phenotype_data.csv', index_col=0)
omics_proteins = pd.read_csv('omics_proteins.csv', index_col=0)
omics_metabolites = pd.read_csv('omics_metabolites.csv', index_col=0)
clinical_data = pd.read_csv('clinical_data.csv', index_col=0)

# Step 2: Network Construction
smccnet = SmCCNet(
    phenotype_df=phenotype_data,
    omics_dfs=[omics_proteins, omics_metabolites],
    data_types=["protein", "metabolite"],
    kfold=5,
    summarization="PCA",
)
adjacency_matrix = smccnet.run()
print("Adjacency matrix generated.")

# Step 3: Disease Prediction (DPMON)
dpmon = DPMON(
    adjacency_matrix=adjacency_matrix,
    omics_list=[omics_proteins, omics_metabolites],
    phenotype_data=phenotype_data,
    clinical_data=clinical_data,
    model="GCN",
)
predictions = dpmon.run()
print("Disease phenotype predictions:\n", predictions)

BioNeuralNet Overview: Multi-Omics Integration with Graph Neural Networks

BioNeuralNet offers five core steps in a typical workflow:

1. Graph Construction:

   • Not performed internally. You provide or build adjacency matrices externally (e.g., via WGCNA, SmCCNet, or your own scripts).

   • All modules are designed to integrate seamlessly with pandas—most functions offer options to return results as pandas DataFrames, enabling you to incorporate BioNeuralNet outputs directly into your existing workflows.

2. Graph Clustering:

   • Identify functional modules or communities using correlated clustering methods (e.g., CorrelatedPageRank, CorrelatedLouvain, HybridLouvain) that integrate phenotype correlation to extract biologically relevant modules [1].

   • Clustering modules can return either raw partitions or induced subnetwork adjacency matrices (as DataFrames) for visualization.

3. Network Embedding:

   • Generate embeddings using methods such as GCN, GAT, GraphSAGE, and GIN.

   • Outputs can be obtained as native tensors or converted to pandas DataFrames for easy analysis and visualization.

4. Subject Representation:

   • Integrate node embeddings back into omics data to enrich subject-level profiles by weighting features with learned embedding scalars.

   • The result can be returned as a DataFrame or a tensor, fitting naturally into downstream analyses.

5. Downstream Tasks:

   • Execute end-to-end pipelines for disease prediction using DPMON [2].

   • DPMON supports hyperparameter tuning—when enabled, it finds the best configuration and then performs standard training to produce final predictions as a pandas DataFrame.

   • This approach, along with the native pandas integration across modules, ensures that BioNeuralNet can be easily incorporated into your analysis workflows.

Contents:

• Installation
• TOPMED Presentation
  • Demonstrates a step-by-step guide to using BioNeuralNet for multi-omics analysis and research.
• GNN Embeddings for Multi-Omics
  • Graph Convolutional Network (GCN)
  • Graph Attention Network (GAT)
  • GraphSAGE
  • Graph Isomorphism Network (GIN)
  • Task-Driven (Supervised/Semi-Supervised) GNNs
  • Key Insights into GNN Parameters and Outputs
  • Dimensionality Reduction: PCA vs. Autoencoders
  • How DPMON Uses GNNs Differently
  • Example Usage
• Correlated Clustering Methods
  • Overview
  • Mathematical Formulation
  • Graphical Comparison
  • Integration with BioNeuralNet
  • Notes for Users
  • Conclusion
• Tutorials
  • Example 1: SmCCNet + GNN Embeddings + Subject Representation
  • Example 2: SmCCNet + DPMON for Disease Prediction
• Tools
  • Graph Embeddings
  • Subject Representation
  • Disease Prediction (DPMON)
  • Disease Prediction
  • Graph Clustering
• External Tools
  • Graph Construction
• Coming Soon: Multi-Modal Support
  • Overview
  • CPTAC Example
  • Usage Example: Clear Cell Renal Cell Carcinoma
  • Output Examples
  • Integration with BioNeuralNet
  • Integration with Other Data Sources
  • Conclusion
  • References
• User API
  • bioneuralnet.external_tools
  • bioneuralnet.network_embedding
  • bioneuralnet.clustering
  • bioneuralnet.subject_representation
  • bioneuralnet.downstream_task
  • bioneuralnet.utils
  • bioneuralnet.datasets
  • bioneuralnet.metrics
  • Executables
• Frequently Asked Questions (FAQ)

Indices and References

• Index

• Module Index

• Search Page

[1]

Abdel-Hafiz, M., Najafi, M., et al. “Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.” Frontiers in Big Data, 5 (2022). DOI: 10.3389/fdata.2022.894632.

[2]

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.