ArticleNature communications2025
GAUDI: interpretable multi-omics integration with UMAP embeddings and density-based clustering.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- Research trends in cytokine regulation of immunotherapy in non-small cell lung cancer: a bibliometric and BERTopic analysis from 2015 to 2025.Journal of thoracic disease · 2026Article
- Article
- Benchmarking computational methods for multi-omics biomarker discovery in cancer.Briefings in bioinformatics · 2026Article
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- Association between UMAP-identified body composition phenotypes and metabolic dysfunction-associated steatotic liver disease in the general population of China.Frontiers in nutrition · 2026Article
- Omics landscapes in molecular mechanisms withFood chemistry. Molecular sciences · 2025Review
- SIRT4 Controls Macrophage Function and Wound Healing through Control of Protein Itaconylation in Mice.bioRxiv : the preprint server for biology · 2025Article
- A multiple filter-wrapper feature selection algorithm based on process optimization mechanism for high-dimensional omics data analysis.PloS one · 2025Article
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Abstract
Integrating high-dimensional cellular multi-omics data is crucial for understanding various layers of biological control. Single 'omic methods provide important insights, but often fall short in handling the complex relationships between genes, proteins, metabolites and beyond. Here, we present a novel, non-linear, and unsupervised method called GAUDI (Group Aggregation via UMAP Data Integration) that leverages independent UMAP embeddings for the concurrent analysis of multiple data types. GAUDI uncovers non-linear relationships among different omics data better than several state-of-the-art methods. This approach not only clusters samples by their multi-omic profiles but also identifies latent factors across each omics dataset, thereby enabling interpretation of the underlying features contributing to each cluster. Consequently, GAUDI facilitates more intuitive, interpretable visualizations to identify novel insights and potential biomarkers from a wide range of experimental designs.
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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.