Multimodal clustering (MMC) overcomes the limitations of unimodal methods by integrating information from multiple sources, but the complexity of heterogeneous information coupling hinders effective feature extraction. Critically, existing MMC paradigms primarily focus on capturing consensus through coarse-grained cross-modal alignment. However, such task-agnostic strategies overlook the differences in the utility of feature information across varying task environments. In the absence of task-centric guidance, models often struggle to effectively distinguish task-relevant critical information from task-irrelevant redundant noise during the disentanglement process, leading to information confusion in the representation space. To address this challenge, we propose a deep disentangled multimodal clustering method guided by information theory, named DRLMMC, which employs a tripartite information optimization mechanism to achieve deep disentanglement of cross-modal representations. 1) We design modality-specific encoders to construct nonlinear mapping spaces, transforming the reconstruction mechanism of autoencoders into an information-theoretic mutual information (MI) constraint problem, preserving the unique features of different modalities; 2) To establish cross-modal semantic associations, it constructs a cross-modal shared information extraction module, and, based on an information-theoretic framework, designs an optimization objective function to progressively align multimodal feature subspaces through MI maximization and contrastive learning, capturing task-relevant invariant features across modalities; 3) A unique information dynamic perception module is proposed, which employs a conditional MI projection network combined with learning distribution regularization to adaptively extract and enhance modality-specific task-relevant unique information. Experimental results demonstrate that DRLMMC outperforms existing state-of-the-art methods on multimodal benchmark datasets, exhibiting excellent generalization ability. Notably, it achieves precise disentanglement of cross-omics features in multi-omics analysis, offering a novel methodological approach for handling complex biomedical data.

The research on identifying drug-disease associations (DDAs) is widely used in scenarios such as drug development, clinical decision-making, and drug repurposing, holding significant biological and medical significance. Existing methods for drug-disease association prediction have achieved decent performance, they primarily rely on simplistic drug-disease association graphs or similarity graphs. These methods often struggle to capture the high-order correlations of complex multimodal data, limiting their ability to handle the complexity of data associations effectively. In addition, real drug-disease associations are highly sparse, posing a significant challenge to prediction accuracy. To tackle these issues, we propose a general hypergraph neural network framework for drug-disease association prediction based on hierarchical contrastive learning and cross-attention learning. It leverages hypergraph neural networks to learn representations of drugs and diseases carrying high-order correlations and strengthens representation quality using interactive attention learning and hierarchical contrastive learning. Meanwhile, the $\lambda$-weighted loss function is utilized to adapt to the high sparsity property of real drug-disease associations during model training and improve prediction performance. Extensive experiments demonstrate that DD-HGNN$^+$ surpasses other state-of-the-art methods in predicting drug-disease associations and further validation through case studies on Leukemia and Colorectal Neoplasms underscores its reliability.