Collaborative Attention Contrast Learning for Cancer Subtype Identification Based on Multi-Omics Data.

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. This endeavor significantly relies on multi-omics data, whose pivotal role in subtype classification has been widely recognized and applied in current biomedical research and clinical practice. Intricate high-dimensional data in each omics contain significant number of discriminative features as well as noise information. Meanwhile, the involvement of diverse omics in classification tasks varies significantly, contributing to varying degrees of importance for the classification tasks. To take advantage of the unique discriminative information embedded in multi omics data, it is necessary to integrate multi-omics data into a feature space that emphasizes discriminative information maximally, while effectively disregarding irrelevant information. In this work, we propose Collaborative Attention Contrast Learning (CACL) framework, which integrates a genetic attention module (GAM) to capture key intra-omics features and an omics attention module (OAM) to enhance inter-omics relationships and optimizes the collaborative attention models through the strategic utilization of a contrastive loss function. This optimization strategy empowers the algorithm to extract multi-omics fusion features with enhanced discriminative ability, ultimately leading to a remarkable improvement in clustering performance. Experiments conducted on several representative multi-omics cancer datasets have demonstrated that our proposed method outperforms a number of state-of-the-art methods. Furthermore, the findings indicate that our method is capable of identifying clinically significant subgroups across diverse cancer types.