BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909708845056000 |
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| author | Ramos, Vicente Hussein, Sundous Abdel-Hafiz, Mohamed Sarkar, Arunangshu Liu, Weixuan Kechris, Katerina J. Bowler, Russell P. Lange, Leslie Banaei-Kashani, Farnoush |
| author_facet | Ramos, Vicente Hussein, Sundous Abdel-Hafiz, Mohamed Sarkar, Arunangshu Liu, Weixuan Kechris, Katerina J. Bowler, Russell P. Lange, Leslie Banaei-Kashani, Farnoush |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool Ramos, Vicente Hussein, Sundous Abdel-Hafiz, Mohamed Sarkar, Arunangshu Liu, Weixuan Kechris, Katerina J. Bowler, Russell P. Lange, Leslie Banaei-Kashani, Farnoush Machine Learning Genomics 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. |
| title | BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2507.20440 |