  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.
