MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder

• URL: http://arxiv.org/abs/2510.06880v1
• Date: Wed, 08 Oct 2025 10:48:15 GMT
• Title: MoRE-GNN: Multi-omics Data Integration with a Heterogeneous Graph Autoencoder
• Authors: Zhiyu Wang, Sonia Koszut, Pietro Liò, Francesco Ceccarelli,
• Abstract summary: MoRE-GNN captures biologically meaningful relationships and outperforms existing methods.<n>MoRE-GNN offers an adaptive, scalable and interpretable framework for advancing multi-omics integration.
• Score: 15.89170003903628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of multi-omics single-cell data remains challenging due to high-dimensionality and complex inter-modality relationships. To address this, we introduce MoRE-GNN (Multi-omics Relational Edge Graph Neural Network), a heterogeneous graph autoencoder that combines graph convolution and attention mechanisms to dynamically construct relational graphs directly from data. Evaluations on six publicly available datasets demonstrate that MoRE-GNN captures biologically meaningful relationships and outperforms existing methods, particularly in settings with strong inter-modality correlations. Furthermore, the learned representations allow for accurate downstream cross-modal predictions. While performance may vary with dataset complexity, MoRE-GNN offers an adaptive, scalable and interpretable framework for advancing multi-omics integration.

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