BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool

Welcome to BioNeuralNet 1.1.4

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.

Citation

If you use BioNeuralNet in your research, we kindly ask that you cite our paper:

Ramos, V., Hussein, S., et al. (2025). BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool. arXiv preprint arXiv:2507.20440 | DOI: 10.48550/arXiv.2507.20440.

For your convenience, you can use the following BibTeX entry:

BibTeX Citation

@misc{ramos2025bioneuralnetgraphneuralnetwork,
      title={BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool},
      author={Vicente Ramos and Sundous Hussein and Mohamed Abdel-Hafiz and Arunangshu Sarkar and Weixuan Liu and Katerina J. Kechris and Russell P. Bowler and Leslie Lange and Farnoush Banaei-Kashani},
      year={2025},
      eprint={2507.20440},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.20440},
}

Documentation

For complete documentation, tutorials, and examples, please visit our Read the Docs site: bioneuralnet.readthedocs.io

Table of Contents

• 1. Installation
  • 1.1. Install BioNeuralNet
  • 1.2. Install PyTorch and PyTorch Geometric
• 2. BioNeuralNet Core Features
• 3. Why Graph Neural Networks for Multi-Omics?
• 4. Example: Network-Based Multi-Omics Analysis for Disease Prediction
• 5. Explore BioNeuralNet's Documentation
• 6. Acknowledgments
• 7. Contributing
• 8. License
• 9. Contact
• 10. References

1. Installation

BioNeuralNet is available as a package on the Python Package Index (PyPI), making it easy to install and integrate into your workflows.

1.1. Install BioNeuralNet

pip install bioneuralnet

PyPI Project Page: https://pypi.org/project/bioneuralnet/

Requirements: BioNeuralNet is tested and supported on Python versions 3.10, 3.11, and 3.12. Functionality on other versions is not guaranteed.

1.2. Install PyTorch and PyTorch Geometric

BioNeuralNet relies on PyTorch for GNN computations. Install PyTorch separately:

• PyTorch (CPU):

  pip install torch torchvision torchaudio

• PyTorch Geometric:

  pip install torch_geometric

For GPU acceleration, please refer to:

• PyTorch Installation Guide
• PyTorch Geometric Installation Guide

2. BioNeuralNet Core Features

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:

• Graph construction:

  • Similarity graphs: k-NN (cosine/Euclidean), RBF, mutual information.

  • Correlation graphs: Pearson, Spearman; optional soft-thresholding.

  • Phenotype-aware graphs: SmCCNet integration (R) for sparse multiple canonical-correlation networks.

• Preprocessing Utilities:

  • RData conversion to pandas DataFrame: Converts an RData file to CSV and loads it into a pandas DataFrame.

  • Top‑k variance‑based filtering: Cleans data and selects the top‑k numeric features by variance.

  • Random forest feature selection: Fits a RandomForest and returns the top‑k features by importance.

  • ANOVA F‑test feature selection: Runs an ANOVA F‑test with FDR correction and selects significant features.

  • Network pruning by edge‑weight threshold: Removes edges below a weight threshold and drops isolated nodes.

• GNN Embeddings: Transform complex biological networks into versatile embeddings, capturing both structural relationships and molecular interactions.

• Downstream Tasks:

  • Subject representation: 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.

BioNeuralNet emphasizes usability, reproducibility, and adaptability, making advanced network-based multi-omics analyses accessible to researchers working in precision medicine and systems biology.

3. Why Graph Neural Networks for Multi-Omics?

Traditional machine learning methods often struggle with the complexity and high dimensionality of multi-omics data, particularly their inability to effectively capture intricate molecular interactions and dependencies. BioNeuralNet overcomes these limitations by using graph neural networks (GNNs), which naturally encode biological structures and relationships.

BioNeuralNet supports several state-of-the-art GNN architectures optimized for biological applications:

• Graph Convolutional Networks (GCN): Aggregate biological signals from neighboring molecules, effectively modeling local interactions such as gene co-expression or regulatory relationships.

• Graph Attention Networks (GAT): Use attention mechanisms to dynamically prioritize important molecular interactions, highlighting the most biologically relevant connections.

• GraphSAGE: Facilitate inductive learning, enabling the model to generalize embeddings to previously unseen molecular data, thereby enhancing predictive power and scalability.

• Graph Isomorphism Networks (GIN): Provide powerful and expressive graph embeddings, accurately distinguishing subtle differences in molecular interaction patterns.

For detailed explanations of BioNeuralNet's supported GNN architectures and their biological relevance, see GNN Embeddings

4. Example: Network-Based Multi-Omics Analysis for Disease Prediction

• Data Preparation:

  • Load your multi-omics data (e.g., transcriptomics, proteomics) along with phenotype and clinical covariates.

• Network Construction:

  • Here, we construct the multi-omics network using an external R package, SmCCNet 1
  • BioNeuralNet provides convenient wrappers for external tools (like SmCCNet) through bioneuralnet.external_tools. Note: R must be installed for these wrappers.

• Disease Prediction with DPMON:

  • DPMON 2 integrates omics data and network structures to predict disease phenotypes.
  • It provides an end-to-end pipeline, complete with built-in hyperparameter tuning, and outputs predictions directly as pandas DataFrames for easy interoperability.

Example Usage:

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

# Load the dataset and access individual omics modalities
example = DatasetLoader("example1")
omics_genes = example.data["X1"]
omics_proteins = example.data["X2"]
phenotype = example.data["Y"]
clinical = example.data["clinical_data"]

# Network Construction with SmCCNet
smccnet = SmCCNet(
    phenotype_df=phenotype,
    omics_dfs=[omics_genes, omics_proteins],
    data_types=["Genes", "Proteins"],
    kfold=5,
    summarization="PCA",
)
global_network, clusters = smccnet.run()
print("Adjacency matrix generated." )

# Disease Prediction using DPMON
dpmon = DPMON(
    adjacency_matrix=global_network,
    omics_list=[omics_genes, omics_proteins],
    phenotype_data=phenotype,
    clinical_data=clinical,
    model="GCN",
    repeat_num=5,
    tune=True,
    gpu=True,
    cuda=0,
    output_dir="./output"
)

predictions, avg_accuracy = dpmon.run()
print("Disease phenotype predictions:\n", predictions)

5. Explore BioNeuralNet's Documentation

For detailed examples and tutorials, visit:

• Quick Start: A quick walkthrough demonstrating the BioNeuralNet workflow from start to finish.

• TCGA-BRCA Demo: A detailed real-world example applying BioNeuralNet to breast cancer subtype prediction.

6. Acknowledgments

BioNeuralNet integrates multiple open-source libraries. We acknowledge key dependencies:

• PyTorch: GNN computations and deep learning models.
• PyTorch Geometric: Graph-based learning for multi-omics.
• NetworkX: Graph data structures and algorithms.
• Scikit-learn: Feature selection and evaluation utilities.
• Pandas & Numpy: Core data processing tools.
• Scipy: Correlation based metrics.
• ray[tune]: Hyperparameter tuning for GNN models.
• matplotlib: Data visualization.
• python-louvain: Community detection algorithms.
• statsmodels: Statistical models and hypothesis testing (e.g., ANOVA, regression).

We also acknowledge R-based tools for external network construction:

• SmCCNet: Sparse multiple canonical correlation network.

7. Contributing

We welcome issues and pull requests! Please:

• Fork the repo and create a feature branch.
• Add tests and documentation for new features.
• Run the test suite and pre-commit hooks before opening a PR.

Developer setup:

git clone https://github.com/UCD-BDLab/BioNeuralNet.git
cd BioNeuralNet
pip install -r requirements-dev.txt
pre-commit install
pytest --cov=bioneuralnet

8. License

BioNeuralNet is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). See the LICENSE file for details.

9. Contact

• Issues and Feature Requests: Open an Issue
• Email: vicente.ramos@ucdenver.edu

10. References

[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

[3] Liu, W., Vu, T., Konigsberg, I. R., Pratte, K. A., Zhuang, Y., & Kechris, K. J. (2023). "Network-Based Integration of Multi-Omics Data for Biomarker Discovery and Phenotype Prediction." Bioinformatics, 39(5), btat204. DOI: 10.1093/bioinformatics/btat204

11. Citation

If you use BioNeuralNet in your research, we kindly ask that you cite our paper:

Vicente Ramos, et al. (2025). BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool. arXiv preprint arXiv:2507.20440.

For your convenience, you can use the following BibTeX entry:

BibTeX Citation

@misc{ramos2025bioneuralnetgraphneuralnetwork,
      title={BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool},
      author={Vicente Ramos and Sundous Hussein and Mohamed Abdel-Hafiz and Arunangshu Sarkar and Weixuan Liu and Katerina J. Kechris and Russell P. Bowler and Leslie Lange and Farnoush Banaei-Kashani},
      year={2025},
      eprint={2507.20440},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2507.20440},
}