ArticleiScience2023
MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Consistency of feature attribution in deep learning architectures for multi-omics.Scientific reports · 2026Article
- Prevalence aware feature selection improves biomarker identification in microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- A powerful representation learning method for enhanced analysis of incomplete multi-omics data.NPJ systems biology and applications · 2026Article
- SCONE: a subset-contrastive method for multi-omics network embedding.Briefings in bioinformatics · 2026Article
- A unified framework for correcting batch effects and integrating multi-omics data.Scientific reports · 2026Article
- MMPCS: multi-view molecular pretraining based on consistency information and specific information.Bioinformatics (Oxford, England) · 2026Article
- Decoupled contrastive multi-view clustering with adaptive false negative elimination for cancer subtyping.PLoS computational biology · 2025Article
- JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- A multi-omics integration framework using multi-label guided learning and multi-scale fusion.Briefings in bioinformatics · 2025Article
- Autoencoders with shared and specific embeddings for multi-omics data integration.BMC bioinformatics · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.Briefings in bioinformatics · 2024Article
- Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.Briefings in bioinformatics · 2024Article
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation.Bioinformatics (Oxford, England) · 2024Article
- Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.Briefings in bioinformatics · 2024Article
- Improving reduced-order models through nonlinear decoding of projection-dependent outputs.Patterns (New York, N.Y.) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.