MOHGCN: A trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis

With the advancement of cutting-edge sequencing methodologies, the integration of multi-omics data provides invaluable opportunities for researchers to study complex diseases from a molecular perspective while at the same time being challenged by the deployment of safety-critical applications such as computer-aided diagnostics. However, existing methods in multi-omics data integration primarily explore interactions between omics or samples, neglecting high-order interaction information among biomolecules specific to certain diseases. In this work, we propose MOHGCN, a trustworthy multi-omics data integration framework based on specificity-aware heterogeneous graph convolutional neural networks for disease diagnosis, aiming to maximize the utilization of biomolecular interactions in patients with specific diseases for precise diagnosis to enhance the model’s credibility. In the approach, we constructed a heterogeneous graph of samples and genes and devised the HGCN graph convolution model specifically tailored to the sample–gene heterogeneous graph. Concurrently, techniques such as trustworthy attention weights and self-attention mechanisms were incorporated to unveil relationships between different omics, facilitating the efficient integration of multi-omics data. Through comprehensive experimentation on four publicly available multi-omics medical datasets, our proposed framework consistently demonstrates superior performance across various classification tasks. Simultaneously, the experimental results substantiate the model’s effectiveness in extracting features from multi-omics data and unveiling latent associations among different omics.