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1. DSCC: disease subtyping using spectral clustering and community detection from consensus networks

   https://doi.org/10.1093/bib/bbaf600  

   Tran, Dao; Pham, Van-Dung; Nguyen, Ha; Bya, Phi; Qdaisat, Aiham; Phan, Liem Minh; Yeung, Sai-Ching Jim; Nguyen, Tin (November 2025, Briefings in Bioinformatics)

   Abstract Molecular subtyping is fundamental in cancer research and clinical management of cancer, guiding treatment planning, monitoring therapeutic response, and informing prognosis. Early methods were designed specifically for gene expression data due to the lack of other molecular data types. Thanks to breakthroughs in high-throughput technologies, recent subtyping tools have shifted their focus to integrating multi-omics profiles to uncover novel subtypes that better reflect genetic variation, molecular pathogenesis, tumor heterogeneity, and host response biological mechanisms. However, these integrative approaches have not been able to fully exploit the complementary potentials of diverse molecular data types. They often rely on specific omics types with large common sample size and fail to incorporate important biological knowledge in their models. Here, we introduce Disease subtyping using Spectral clustering and Community detection from Consensus networks (DSCC), a method designed to identify meaningful disease subtypes from a wide range of molecular data, including gene expression, miRNA expression, DNA methylation, copy number variation, somatic mutations, protein abundance, and metabolite levels. We demonstrate the superiority of DSCC over state-of-the-art cancer subtyping methods using 43 cancer datasets with more than 11,000 patients. Furthermore, the incorporation of DSCC-derived subtype information as a covariate in prognostic models improves survival prediction accuracy and robustness. The DSCC source code, data, and scripts for reproducing all results in this study are available at https://github.com/tinnlab/DSCC. 

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2. Sliced inverse regression for integrative multi-omics data analysis

   https://doi.org/10.1515/sagmb-2018-0028  

   Jain, Yashita; Ding, Shanshan; Qiu, Jing (January 2019, Statistical Applications in Genetics and Molecular Biology)

   null (Ed.)

   Abstract Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods. 

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3. Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection

   https://doi.org/10.3390/ijms26041707  

   Pateras, Joseph; Lodi, Musaddiq; Rana, Pratip; Ghosh, Preetam (February 2025, International Journal of Molecular Sciences)

   The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes. 

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4. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction

   https://doi.org/10.1016/j.jbi.2023.104512  

   Ranjbari, Sima; Arslanturk, Suzan (November 2023, Journal of Biomedical Informatics)

   Objective: The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression. Methods: However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models. Results: Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management. Conclusion: The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer. 

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5. X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration

   https://doi.org/10.1093/bioinformatics/btag046  

   Bui, Tien-Thanh; Xie, Rui; Zhang, Wei (February 2026, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract MotivationThe rapid accumulation of multi-omics data presents a valuable opportunity to advance our understanding of complex diseases and biological systems, driving the development of integrative computational methods. However, the complexity of biological processes, spanning multiple molecular layers and involving intricate regulatory interactions, requires models that can capture both intra- and cross-omics relationships. Most existing integration methods primarily focus on sample-level similarities or intra-omics feature interactions, often neglecting the interactions across different omics layers. This limitation can result in the loss of critical biological information and suboptimal performance. To address this gap, we propose X-intNMF, a network-regularized non-negative matrix factorization (NMF) framework that simultaneously integrates intra- and cross-omics feature interactions into a shared low-dimensional representation (see Fig. 1). By modeling these multi-layered relationships, X-intNMF enhances the representation of biological interactions and improves integration quality and prediction accuracy. ResultsFor evaluation, we applied X-intNMF to predict breast cancer phenotypes and classify clinical outcomes in lung and ovarian cancers using mRNA expression, microRNA expression, and DNA methylation data from TCGA. The results show that X-intNMF consistently outperforms state-of-the-art methods. Ablation studies confirm that incorporating both cross-omics and intra-omics interactions contributes significantly to the model’s improved performance. Additionally, survival analysis on 25 TCGA cancer datasets demonstrates that the integrated multi-omics representation offers strong prognostic value for both overall survival and disease-free status. These findings highlight X-intNMF’s ability to effectively model multi-layered molecular interactions while maintaining interpretability, robustness, and scalability within the NMF framework. Availability and implementationThe source code and datasets supporting this study are publicly available at GitHub (https://github.com/compbiolabucf/X-intNMF) and archived on Zenodo (https://doi.org/10.5281/zenodo.18238385). 

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