MOIRA模型能有效整合缺失数据的多组学信息，提升阿尔茨海默病预测精度。
Robust Multi-Omics Integration from Incomplete Modalities Significantly Improves Prediction of Alzheimer's Disease
- 通过共享嵌入空间和自适应加权融合，实现对不完整组学数据的鲁棒整合
- 在ROSMAP数据集上优于现有方法，预测准确率显著提升
- 可识别已知阿尔茨海默病生物标志物，适合多组学疾病预测研究者
多组学数据能揭示复杂的分子互作机制并提供代谢与疾病线索。然而，不同组学数据的缺失严重阻碍了跨异质组学的整合分析。为此，我们提出MOIRA（Multi-Omics Integration with Robustness to Absent modalities），一种早期整合方法，通过表示对齐与自适应聚合，实现对不完整组学数据的鲁棒学习。MOIRA通过将每类组学数据投影至共享嵌入空间，并采用可学习加权机制进行融合，利用所有样本（包括存在缺失模态的样本）。在阿尔茨海默病的宗教团体研究与记忆老化项目（ROSMAP）数据集上的评估显示，MOIRA优于现有方法，且消融实验验证了各模态的贡献。特征重要性分析揭示了与已有文献一致的阿尔茨海默病相关生物标志物，凸显该方法的生物学意义。
原文摘要 · Abstract (English)
Multi-omics data capture complex biomolecular interactions and provide insights into metabolism and disease. However, missing modalities hinder integrative analysis across heterogeneous omics. To address this, we present MOIRA (Multi-Omics Integration with Robustness to Absent modalities), an early integration method enabling robust learning from incomplete omics data via representation alignment and adaptive aggregation. MOIRA leverages all samples, including those with missing modalities, by projecting each omics dataset onto a shared embedding space where a learnable weighting mechanism fuses them. Evaluated on the Religious Order Study and Memory and Aging Project (ROSMAP) dataset for Alzheimer's Disease (AD), MOIRA outperformed existing approaches, and further ablation studies confirmed modality-wise contributions. Feature importance analysis revealed AD-related biomarkers consistent with prior literature, highlighting the biological relevance of our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。