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

• URL: http://arxiv.org/abs/2507.20440v1
• Date: Sun, 27 Jul 2025 23:21:04 GMT
• Title: BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool
• Authors: Vicente Ramos, Sundous Hussein, Mohamed Abdel-Hafiz, Arunangshu Sarkar, Weixuan Liu, Katerina J. Kechris, Russell P. Bowler, Leslie Lange, Farnoush Banaei-Kashani,
• Abstract summary: BioNeuralNet is a Python framework designed for end-to-end network-based multi-omics data analysis.<n>It 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.<n>BioNeuralNet is an open-source, user-friendly, and extensively documented framework designed to support flexible and reproducible multi-omics network analysis in precision medicine.
• Score: 0.9674145073701151
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 entities. 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.

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