OmniCLIC: A Unified Omics Contrastive Learning Framework for Effective Integration and Classification of Multiomics Data

Integrating multiomics data for cancer subtype classification remains a critical yet challenging task due to the high dimensionality, heterogeneity, and limited interpretability of omics features. To address these limitations, we propose OmniCLIC, a unified Omics Contrastive Learning and Integration Classification framework that enables end-to-end multiomics integration, feature learning, and prediction. Three key components are proposed in OmniCLIC: OmniNet, a customized MLP with feature-wise scaling, neural tangent parametrization, and regularization strategies for omics-specific representation learning; a contrastive learning module that jointly optimizes supervised contrastive and cross-entropy losses; and OCDN, a decision-level fusion module that captures interomics correlations via a cross-modal correlation tensor to enhance generalization. We evaluate OmniCLIC on four benchmark cancer data sets, where it consistently outperforms state-of-the-art methods in accuracy and robustness across both binary and multiclass multiomics data sets. Furthermore, OmniCLIC enables biologically meaningful interpretation by identifying key molecular features through its built-in scaling layers. Functional enrichment analyses on the selected features reveal subtype-specific pathways, such as epithelial morphogenesis and PI3K-Akt signaling, aligned with known cancer biology. We further extend OmniCLIC to single-cell multiomics data (RNA+ATAC, RNA+ADT), where it still outperforms existing methods, verifying the framework's generalizability across multiomics data at different scales.