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

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ID: 317202
2026
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Abstract
SUMMARY: Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical challenges. Network-based approaches have advanced multi-omics research by effectively capturing biologically relevant relationships among molecular features (e.g., genes, proteins, metabolites). While these methods are powerful for representing molecular interactions, there remains a need for tools specifically designed to effectively utilize these network representations across diverse downstream analyses. To fulfill this need, we introduce BioNeuralNet, a flexible and modular Python framework tailored for end-to-end network-based multi-omics data analysis. BioNeuralNet leverages Graph Neural Networks (GNNs) to learn biologically meaningful low-dimensional representations from multi-omics networks, converting these complex molecular networks into versatile embeddings. BioNeuralNet supports all major stages of multi-omics network analysis, including several network construction techniques, generation of low-dimensional representations, and a broad range of downstream analytical tasks. Its extensive utilities, including diverse GNN architectures, and compatibility with established Python packages (e.g., scikit-learn, PyTorch, NetworkX), enhance usability and facilitate quick adoption. BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine. AVAILABILITY AND IMPLEMENTATION: The BioNeuralNet library is available via The Python Package Index (PyPI). Source code, documentation, tutorials, and workflows are hosted at https://bioneuralnet.readthedocs.io. Code archived at https://doi.org/10.5281/zenodo.17503083.
Reference Key
openalex_W4412823093 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vicente Ramos‐Santillan, Sundous Hussein, Mohamed Abdel-Hafiz, Arunangshu Sarkar, Weixuan Liu, Katerina Kechris, Russell P. Bowler, Leslie A. Lange, Farnoush Banaei‐Kashani
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag365
URL
https://doi.org/10.1093/bioinformatics/btag365
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