Integration of data from multiple omics techniques is becoming increasingly
important in biomedical research. Due to non-uniformity and technical
limitations in omics platforms, such integrative analyses on multiple omics,
which we refer to as views, involve learning from incomplete observations with
various view-missing patterns. This is challenging because i) complex
interactions within and across observed views need to be properly addressed for
optimal predictive power and ii) observations with various view-missing
patterns need to be flexibly integrated. To address such challenges, we propose
a deep variational information bottleneck (IB) approach for incomplete
multi-view observations. Our method applies the IB framework on marginal and
joint representations of the observed views to focus on intra-view and
inter-view interactions that are relevant for the target. Most importantly, by
modeling the joint representations as a product of marginal representations, we
can efficiently learn from observed views with various view-missing patterns.
Experiments on real-world datasets show that our method consistently achieves
gain from data integration and outperforms state-of-the-art benchmarks.

本研究提出了一种基于深度可变信息瓶颈方法的不完整多视图观测数据的处理方法，该方法能够有效地解决来自不同视图的数据不均匀性和技术限制等难题，实现数据的高效整合、建模和预测。