Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by analyzing multi-omics data. However, the high noise levels in omics data often pose challenges in capturing consistent representations and adequately integrating their information. This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR). Firstly, multiple independent variational autoencoders and contrastive loss functions were designed to separate noise from omics data and capture latent consistent representations. Subsequently, an Attention Deep Integration Network was proposed to integrate consistent representations across different omics levels effectively. Additionally, we introduced the Improved Deep Embedded Clustering algorithm to make integrated variable clustering friendly. The effectiveness of DILCR was evaluated using 10 typical cancer datasets from The Cancer Genome Atlas and compared with 14 state-of-the-art integration methods. The results demonstrated that DILCR effectively captures the consistent representations in omics data and outperforms other integration methods in cancer subtyping. In the Kidney Renal Clear Cell Carcinoma case study, cancer subtypes were identified by DILCR with significant biological significance and interpretability.

Multi-omics cancer subtype prediction can identify cancer subtypes effectively and has the advantage of correlating genotype and phenotype. However, insufficient exploration of the correlation of information among different omics levels may lead to poor prediction of cancer subtypes. To address this issue, we propose a novel framework, termed Multi-Omics correlation reconstruction (MOCR), which performs reconstruction of complete graph forms based on a self-expressive learning network. Specifically, MOCR first employs autoencoders to unify the dimensions across different omics types. It then leverages parallel query and key networks (QKNets) to learn representations for each omics. These representations are passed into a correlation reconstruction module (CRModule), which computes self-expressive coefficients that jointly capture omics-self characteristics and inter-omics relationships. QKNets and CRModule form a correlative self-expressive learning network, enabling better utilization of the advantages of multi-omics. Importantly, the CRModule's complete graph reconstruction of omics correlations models each omics pair exactly once, thereby avoiding redundancy. Finally, spectral clustering is applied to derive cancer subtypes. We have evaluated our method on nine TCGA cancer datasets and three simulation datasets. The results showed that the MOCR had significant advantages in cancer subtype identification. The complete code is available at https://github.com/JerryZ09/MOCR.

Numerous studies have demonstrated that microRNAs (miRNAs) play crucial roles in the development and progression of various diseases, making the identification of miRNA-disease associations (MDAs) essential for understanding human disease etiology. While several computational models have been developed to predict MDAs, challenges persist—particularly the limited consideration of information interactions among multi-source similarities and the presence of “false-negative” associations in the original topology. To address these issues, we propose ISFNMDA, a model designed to infer potential MDAs by leveraging multi-view collaborative learning for feature extraction and optimizing association topology through graph structure momentum contrastive learning. Specifically, multi-source similarities of miRNAs and diseases are mapped into a unified feature space via encoders. The Pearson correlation coefficient is employed to derive pairwise constraints between nodes, facilitating information interactions and constructing interval-shared information constraints. Subsequently, an inference graph learner models the representations to generate an inferred graph topology. By maximizing mutual information between the inferred topology and the original “false-negative” associations through momentum contrastive learning, the model effectively reduces spurious correlations. The final comprehensive representations and optimized graph structure are then used to predict potential MDAs. Experimental results demonstrate that ISFNMDA outperforms existing methods, and case studies further validate its predictive capability. The complete code and related materials for ISFNMDA is available at https://github.com/WDNokl/ISFNMDA.

Motivation: Enzyme-reaction retrieval is increasingly used to prioritize candidate biocatalysts for experimental follow-up, where useful recommendations should indicate not only whether an enzyme can catalyze a target reaction but also under which pH and temperature conditions it should be tested. Existing retrieval models optimize catalytic matching scores, whereas catalytic optima predictors are typically developed as enzyme-level regressors because public pH and temperature annotations are sparse and often available only at the enzyme or EC-associated record level. This separation leaves a practical gap: high-ranking enzyme-reaction pairs are not evaluated for condition suitability, and enzyme-level optima predictions do not use the reaction context being retrieved. Results: We present GERO, a multimodal fusion framework that uses feature-gated cross-modal fusion to integrate global enzyme sequence semantics, sequence-derived pocket geometry, and molecular reaction representations for condition-aware enzyme-reaction retrieval and catalytic optima estimation with reaction context. To evaluate this setting, we define the tolerance-restricted hit rate (Hit@k-TR), which requires both top-k retrieval of the correct candidate and condition prediction within predefined tolerances. Across enzyme- and reaction-similarity splits, GERO improves Hit@k-TR over two-stage retrieval-then-prediction baselines. Representative benchmark examples and an iodinin biosynthesis case study further illustrate GERO’s ability to provide candidate rankings together with plausible assay-condition estimates for downstream experimental prioritization. Availability and implementation: Source code is available at https://github.com/ykxhs/GERO . Contact: liujuan@whu.edu.cn Supplementary information: Supplementary data are available at XXXX online.