SMODA: Interpretable Multimodal Omics Integration for Disease Classification and Subtype Discovery via Heterogeneous Transfer Learning

Disease heterogeneity leads to various clinical molecular
subtypes,
further limiting the development of precision medicine. Multimodal
data integration shows promise for addressing this challenge, but
existing methods are affected by omics noise, data imbalance, and
limited interpretability. Here, we propose SMODA (Semi-Supervised
Multimodal Omics Data Analysis), which is a flexible framework to
integrate multimodal omics data by combining heterogeneous transfer
learning and semisupervised modeling. SMODA learns shared latent representations
across different modalities to reduce the cross-modal heterogeneity.
Systematic benchmarking demonstrates that SMODA outperforms existing
multiomics integration methods both in disease classification and
subtype identification. The application of SMODA in a multimodal esophageal
cancer data set still shows better classification performance. A previously
unrecognized disease subtype is also identified. This subtype shows
altered lipid metabolism, inflammatory responses, and distinct exposure
features, which are also linked to poor clinical outcomes. SMODA provides
a reliable and interpretable framework for multimodal data integration
and supports clinically relevant disease stratification. The SMODA
framework is available at https://github.com/zhaoxiaoqi0714/SMODA.git.