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.

Understanding complex diseases hinges on a profound understanding of intricate biomolecular interactions unfolding within a complex, multidimensional landscape, challenging traditional methods to extract meaningful insights. While multi-omics networks capture the richness of biological data, providing a basis for predicting relationships between biomolecules and various phenotypic traits of complex diseases, their inherent complexity limits their predictive power. To address this challenge, we introduce a novel pipeline that leverages the power of Graph Neural Networks (GNNs) to extract and integrate meaningful information from multi-omics networks. By generating informative node embeddings and seamlessly incorporating them into the original subject-level data, our approach captures both local and global network dependencies, leading to substantial improvements in disease prediction accuracy. The proposed pipeline optimizes the embedding generation process for the specific prediction task, enabling the model to learn task-relevant representations. Through rigorous experimentation, we demonstrate the superior performance of our approach, surpassing existing methods by a substantial margin on nine real-world multi-omics datasets. With remarkable increases in accuracy ranging approximately from 8% to 10% over the best-performing baseline, particularly when the multi-omics networks are moderately dense, striking a balance between capturing complex relationships and avoiding excessive noise. Our findings underscore the potential of GNNs to significantly improve disease prediction by effectively extracting and representing knowledge embedded within multi-omics networks.

The study of complex behavior of biological systems has become increasingly dependent on evolutionary network modeling. In particular, multi-omics networks capture interactions between biomolecules such as proteins and metabolites, providing a basis for predicting relationships between such biomolecules and various phenotypic traits of complex diseases. In this paper, we introduce an integrative framework that given a multi-omics network representing a cohort of subjects, learns expressive representations for network nodes, and combines the learned nodes representations with the biological profiles of individual subjects for enriched representation of the subjects. With extensive empirical evaluation using real-world multi-omics networks, we show that our proposed framework significantly outperforms existing and baseline methods in terms of subject representation accuracy, particularly when the multi-omics network representing the cohort is sparse and structured and therefore, more informative.