Abstract：
The gut microbiome has been regarded as one of the fundamental determinants regulating human health, and multi-omics data profiling has been increasingly utilized to bolster the deep understanding of this complex system. However, stemming from cost or other constraints, the integration of multi-omics often suffers from incomplete views, which poses a great challenge for the comprehensive analysis. In this work, a novel deep model named Incomplete Multi-Omics Variational Neural Networks (IMOVNN) is proposed for incomplete data integration, disease prediction application and biomarker identification. Benefiting from the information bottleneck and the marginal-to-joint distribution integration mechanism, the IMOVNN can learn the marginal latent representation of each individual omics and the joint latent representation for better disease prediction. Moreover, owing to the feature-selective layer predicated upon the concrete distribution, the model is interpretable and can identify the most relevant features. Experiments on inflammatory bowel disease multi-omics datasets demonstrate that our method outperforms several state-of-the-art methods for disease prediction. In addition, IMOVNN has identified significant biomarkers from multi-omics data sources.
摘要：
肠道微生物组被认为是调节人类健康的基本决定因素之一。和多组学数据分析已越来越多地用于加强对这个复杂系统的深刻理解。然而,源于成本或其他限制，多元组学的整合往往存在观点不完整的问题，这对综合分析提出了很大的挑战。在这项工作中,提出了一种新的深度模型，称为不完全多组学变分神经网络（IMOVNN），用于不完全数据集成，疾病预测应用和生物标志物识别。受益于信息瓶颈和边际向联合配送一体化机制，IMOVNN可以学习每个个体组学的边缘潜在表示和联合潜在表示，以更好地预测疾病。此外，由于基于具体分布的特征选择层，该模型是可解释的，可以识别最相关的特征。对炎症性肠病多组学数据集的实验表明，我们的方法优于几种最新的疾病预测方法。此外,IMOVNN已从多组学数据源中识别出重要的生物标志物。
