{"ID":2890029,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20440","arxiv_id":"2507.20440","title":"BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool","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.","short_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 molecu...","url_abs":"https://arxiv.org/abs/2507.20440","url_pdf":"https://arxiv.org/pdf/2507.20440v1","authors":"[\"Vicente Ramos\",\"Sundous Hussein\",\"Mohamed Abdel-Hafiz\",\"Arunangshu Sarkar\",\"Weixuan Liu\",\"Katerina J. Kechris\",\"Russell P. Bowler\",\"Leslie Lange\",\"Farnoush Banaei-Kashani\"]","published":"2025-07-27T23:21:04Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.GN\"]","methods":"[\"Graph Neural Network\"]","has_code":false}