A multi-omics integration framework using multi-label guided learning and multi-scale fusion

作者：Yuze Li, Yuze Li, Yinghe Wang, Ying Li, Ying Li, Ying Li, Wei Du · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf493 · 被引用次数：6 · 研究领域：Gene expression and cancer classification、Bioinformatics and Genomic Networks、Machine Learning in Bioinformatics

The rapid development of high-throughput sequencing technologies has generated vast amounts of omics data, making multi-omics integration a crucial approach for understanding complex diseases. Despite the introduction of various multi-omics integration methods in recent years, existing approaches still have limitations, primarily in their reliance on manual feature selection, restricted applicability, and inability to comprehensively capture both inter-sample and cross-omics interactions. To address these challenges, we propose mmMOI, an end-to-end multi-omics integration framework that incorporates multi-label guided learning and multi-scale attention fusion. mmMOI directly processes raw high-dimensional omics data without requiring manual feature selection, thereby enhancing model interpretability and eliminating biases introduced by feature preselection. First, we introduce a multi-label guided multi-view graph neural network, which enables the model to adaptively learn omics data representations across different datasets, thereby improving generalizability and stability. Second, we design a multi-scale attention fusion network, which integrates global attention and local attention. This dual-attention mechanism allows mmMOI to more accurately integrate multi-omics data, enhance cross-omics feature representations, and improve classification performance. Experimental results demonstrate that mmMOI significantly outperforms state-of-the-art methods in classification tasks, ...