ArticleNature communications2025
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.
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 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Image feature embedding with a deep learning framework improves genome-wide association studies on dog endophenotypes.Science advances · 2026Article
- Multi-Scale CNN-LSTM for Short-Circuit Fault Diagnosis of Shipboard Power System.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning-Assisted FTIR Spectroscopy Analysis of Kidney Preservation Fluids for Delayed Graft Function Risk Stratification.Journal of clinical medicine · 2026Article
- Interactive visualization of metric distortion in nonlinear data embeddings using the distortions package.Briefings in bioinformatics · 2026Article
- Printing technologies for monitoring crop health.Nature communications · 2026Review
- Optimal transport analysis of high-dimensional flow cytometry data in immuno-oncology.Frontiers in immunology · 2026Article
- Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Visualizing high-dimensional data is essential for understanding biomedical data and deep learning models. Neighbor embedding methods, such as t-SNE and UMAP, are widely used but can introduce misleading visual artifacts. We find that the manifold learning interpretations from many prior works are inaccurate and that the misuse stems from a lack of data-independent notions of embedding maps, which project high-dimensional data into a lower-dimensional space. Leveraging the leave-one-out principle, we introduce LOO-map, a framework that extends embedding maps beyond discrete points to the entire input space. We identify two forms of map discontinuity that distort visualizations: one exaggerates cluster separation and the other creates spurious local structures. As a remedy, we develop two types of point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection, which are validated on datasets from computer vision and single-cell omics.
Indexed as
Identifiers
What OpenQuestion holds
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.