Incomplete multi-view clustering (IMVC) aims to group data into meaningful clusters when each sample is only partially observed across multiple views. Most existing methods either rely on imputation strategies that may introduce noise and distort the underlying data distribution, or adopt cross-view alignment techniques that focus on pairwise relationships, often resulting in suboptimal representations and unstable clustering performance. In this paper, we propose Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering (GAVIM), a novel imputation-free variational framework that enables robust and coherent incomplete multi-view clustering. Specifically, GAVIM leverages mutual information maximization to preserve the high mutual information between the available multi-view data and the shared embedding. Moreover, we explicitly retain local geometric consistency within each view-specific latent space under the guidance of an adaptive global supervision signal. Lastly, GAVIM aligns all views simultaneously using a Gramian representation alignment measure, ensuring coherent structure across modalities and promoting unified, semantically meaningful representations. Extensive experiments on five benchmark IMVC datasets with varying levels of view incompleteness demonstrate that GAVIM consistently outperforms state-of-the-art methods in clustering accuracy and representation quality.

The advancement of sequencing technologies has generated an unprecedented volume of single-cell multi-omics data, providing new opportunities for biological discovery and medical research. However, due to the high heterogeneity across different omics types, effective integration of single-cell multi-omics data remains a critical challenge. Existing methods generally ignore the graph structure information among cells or resort to additional knowledge to construct the cell graphs, leading to suboptimal performance and potentially limited practical utility. In this study, we propose a novel Graph-embedded Deep Generative Clustering model (GeDGC) for single-cell multi-omics data integration. Specifically, GeDGC simultaneously learns the shared latent representations and cluster factors across multiple omics by leveraging Gaussian mixture models. Moreover, we impose the graph embedding constraint on both the latent representations and the cluster assignments to ensure the preservation of intrinsic local data structure among cells. As a result, our model captures complex correlations across omics and obtains informative shared latent embeddings for downstream tasks. Extensive experimental results with seventeen competing methods on ten datasets confirm the superiority of GeDGC in single-cell multi-omics data integration.

Spatial transcriptomics integrates spatial, gene expression, and multichannel immunohistochemistry image data, enabling advanced insights into cellular organization. However, existing methods often struggle to effectively fuse these multimodal data, limiting their potential for accurate spatial domain identification. Here, we propose IE-HERCL (Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning), a novel framework designed to address this challenge. Specifically, IE-HERCL employs hybrid encoding to capture both the non-spatial features and spatial dependencies for both gene and image modalities via autoencoders and GraphSAGE, respectively. These features are then fused using cross-view attention mechanisms to generate the unified informative embedding. To enhance the representation learning capability, we introduce a reinforced contrastive learning strategy to mitigate the influences of false negative samples, where we detect potential positive counterparts with high-order random walks. In addition, the cluster alignment is dynamically refined through optimal transport, which ensures that the fused consensus representation is coherent and robust, enabling accurate spatial domain identification. Our approach achieves state-of-the-art performance on five image-enhanced spatial transcriptomics datasets, demonstrating its robustness and effectiveness in multimodal integration and spatial domain identification. IE-HERCL offers a powerful and innovative solution for advancing spatial transcriptomics analysis. The code is released on https://github.com/wdyi701/IE-HERCL.

Incomplete Multi-view Clustering (IMvC) aims to perform effective clustering in the presence of missing views by exploiting the available information. While many existing approaches demonstrate satisfactory performance, their failure to adequately optimize the recovered data often limits the quality of learned representations and thus hampers clustering performance. To address this challenge, we propose a novel method, Dual-Level Distribution Alignment for Deep Incomplete Multi-View Clustering (DDAIMVC). To effectively address missing data, DDAIMVC employs a fusion-fill strategy to recover incomplete views. The recovered data from each view are then concatenated and processed through an attention mechanism to generate a unified high-level representation. To ensure consistent information across views, the framework performs distribution alignment at both the instance and cluster levels. Specifically, instance-level distribution alignment is conducted by minimizing the maximum mean discrepancy among views, while cluster-level distribution alignment is enhanced via prototypical contrastive learning, which encourages coherent cluster assignments across different modalities. Through the co-optimization of dual-level distribution alignment, the common representation reveals a clear clustering structure. Experimental results on benchmark multi-view datasets demonstrate that DDAIMVC consistently achieves state-of-the-art clustering performance.

Cross-modal representation learning aims to extract semantically aligned representations from heterogeneous modalities such as images and text. Existing multimodal VAE-based models often suffer from limited capability to align heterogeneous modalities or lack sufficient structural constraints to clearly separate the modality-specific and shared factors. In this work, we propose a novel framework, termed **D**isentangled **C**ross-**M**odal Representation Learning with **E**nhanced **M**utual Supervision (DCMEM). Specifically, our model disentangles the common and distinct information across modalities and regularizes the shared representation learned from each modality in a mutually supervised manner. Moreover, we incorporate the information bottleneck principle into our model to ensure that the shared and modality-specific factors encode exclusive yet complementary information. Notably, our model is designed to be trainable on both complete and partial multimodal datasets with a valid Evidence Lower Bound. Extensive experimental results demonstrate significant improvements of our model over existing methods on various tasks including cross-modal generation, clustering, and classification.

Accurate cancer survival prediction remains a critical challenge in clinical oncology, largely due to the complex and multi-omics nature of cancer data. Existing methods often struggle to capture the comprehensive range of informative features required for precise predictions. Here, we introduce PCLSurv, an innovative deep learning framework designed for cancer survival prediction using multi-omics data. PCLSurv integrates autoencoders to extract omics-specific features and employs sample-level contrastive learning to identify distinct yet complementary characteristics across data views. Then, features are fused via a bilinear fusion module to construct a unified representation. To further enhance the model's capacity to capture high-level semantic relationships, PCLSurv aligns similar samples with shared prototypes while separating unrelated ones via prototypical contrastive learning. As a result, PCLSurv effectively distinguishes patient groups with varying survival outcomes at different semantic similarity levels, providing a robust framework for stratifying patients based on clinical and molecular features. We conduct extensive experiments on 11 cancer datasets. The comparison results confirm the superior performance of PCLSurv over existing alternatives. The source code of PCLSurv is freely available at https://github.com/LiangSDNULab/PCLSurv.

MOTIVATION: The high complexity and heterogeneity of cancer pose significant challenges to personalized treatment, making the improvement of cancer survival prediction accuracy crucial for clinical decision-making. The integration of multi-omics data enables a more comprehensive capture of multi-layered information in complex biological processes. However, existing survival analysis models still face limitations in accurately extracting and effectively integrating the unique and shared information from multi-omics data. RESULTS: In this article, we propose a novel prediction model for cancer survival based on soft-label guided contrastive learning and global feature fusion, namely SLCGF. Our model first extracts paired feature representations for each omics using Siamese encoders. We then perform intra-view and inter-view contrastive learning simultaneously, employing a neighborhood-based paradigm to enhance feature discrimination and alignment across omics. To ensure reliable neighbor retention and improve model robustness, we treat the affinities between samples and their high-order neighbors as soft labels to guide the contrastive learning process at both levels. In addition, we adopt a global self-attention mechanism to obtain the unified representation for cancer survival prediction, where the cross-omics connections are fully exploited and complementary information is adaptively integrated. We comprehensively evaluate the performance of our model on 13 cancer multi-omics datasets, and the experimental results demonstrate its superiority over existing approaches. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/LiangSDNULab/SLCGF.

Cancer heterogeneity has posed great challenges in exploring precise therapeutic strategies for cancer treatment. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide new clues on effective clinical therapies. While great efforts have been made, it remains challenging to develop powerful computational methods that can efficiently integrate multi-omics datasets for the task. In this paper, we propose a novel self-supervised learning model called Deep Multi-view Contrastive Learning (DMCL) for cancer subtype identification. Specifically, by incorporating the reconstruction loss, contrastive loss and clustering loss into a unified framework, our model simultaneously encodes the sample discriminative information into the extracted feature representations and well preserves the sample cluster structures in the embedded space. Moreover, DMCL is an end-to-end framework where the cancer subtypes could be directly obtained from the model outputs. We compare DMCL with eight alternatives ranging from classic cancer subtype identification methods to recently developed state-of-the-art systems on 10 widely used cancer multi-omics datasets as well as an integrated dataset, and the experimental results validate the superior performance of our method. We further conduct a case study on liver cancer and the analysis results indicate that different subtypes might have different responses to the selected chemotherapeutic drugs.

Recently, with the exponentially increased amount of multi-view data in various fields such as Multimedia and bioinformatics, multi-view unsupervised feature selection has attracted much attention due to its necessity in dealing with high-dimensional features. Although previous approaches have achieved great success, they generally ignore the consistent information and the high-order connections among views. In this paper, we present a general multi-view unsupervised feature selection model which integrates the common graph learning and feature selection into a unified framework. Specifically, our approach first learns a pseudo label matrix for each view by preserving the local data structure, and then stack them into a third-order tensor with low-rank constraint to explore the high-order connections among the views. In order to exploit the consistent information among different views, we seek a consensus graph matrix with optimal cluster structure by taking advantage of the view-specific pseudo label matrices and the rank constraint. Meanwhile, we adopt the sparse regression model to select discriminative features under the guidance of the final pseudo labels obtained from the learned consensus graph. We introduce an alternate optimization algorithm ground on the alternating direction method of multipliers (ADMM) to optimize the presented method. Extensive experiments on both machine learning and single-cell multi-omics datasets prove the effectiveness of our method. Moreover, the case study carried out on an ovarian cancer dataset further confirms the applicability of our method in identifying non-redundant and representative features.