Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods tend to define the adjacency matrix among samples manually based on meta-features, and then obtain the node embeddings for downstream tasks by Graph Representation Learning (GRL). However, it is not easy for these approaches to generalize to unseen samples. Meanwhile, the complex correlation between modalities is also ignored. As a result, these factors inevitably yield the inadequacy of providing valid information about the patient's condition for a reliable diagnosis. In this paper, we propose an end-to-end Multimodal Graph Learning framework (MMGL) for disease prediction. To effectively exploit the rich information across multi-modality associated with diseases, amodal-attentional multi-modal fusion is proposed to integrate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the adjacency matrix manually as existing methods, the latent graph structure can be captured through a novel way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Unlike the previous transductive methods, our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction problems is then carefully designed and presented, demonstrating that MMGL obtains more favorable performances. In addition, we also visualize and analyze the learned graph structure to provide more reliable decision support for doctors in real medical applications and inspiration for disease research.
翻译:从图表的强大表达能力中获益的图表、基于图表的方法在各种生物医学应用中取得了令人印象深刻的成绩。大多数现有方法倾向于根据元性特征对样本中人工的样本进行定义的匹配矩阵,然后通过“图表代表学习”(GRL)为下游任务获得节点嵌入。然而,这些方法不容易向看不见的样本推广。与此同时,各种模式之间的复杂关联也被忽略。因此,这些因素必然导致无法充分提供关于患者状况的有效信息,以便进行可靠的诊断。在本文中,我们提议建立一个用于疾病预测的端到端多式图表学习框架(MMMGL ) 。要有效地利用与疾病相关的多模式相关的丰富信息进行下游任务中的节点嵌嵌式嵌入。然而,建议这些方法通过利用各种模式之间的相互联系和互补性将每种模式的特征综合起来。此外,这些因素之间的复杂关联性矩阵并非手工界定现有方法,而是可以通过新的适应性图表学习方式来捕捉到潜在图表结构。我们可以与预测模型共同优化,从而揭示与疾病相关的多式多式多式多式模型,从而揭示了与疾病相关的多式的预估测分析模型。