Motivation Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities, but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.

中文翻译：

---

通过可解释的异构集成集成多模态数据


动机整合多模态数据是预测生物医学特征（例如蛋白质功能和疾病结果）的有效方法。然而，现有的数据集成方法不足以解决多模式数据的异构语义。特别是，依赖于统一综合表示的早期和中期方法加强了模式之间的共识，但可能会丢失专有的本地信息。尚未针对生物医学问题系统地研究可以解决这一挑战的替代后期整合方法。