ArticleNeural computation2022
Using Global t-SNE to Preserve Intercluster Data Structure.
Article in Neural computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
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Who cites it
20 citing papers in PubMed.
- In vitro Validation of a Novel Disposable Remover to Remove Activated Leukocytes Generated During Cardiopulmonary Bypass: A Pilot Study.Journal of inflammation research · 2025Trial
- Single-cell atlas of B cell heterogeneity in lacrimal IgG4-related disorders: Unraveling the transition from inflammatory exhaustion to clonal malignancy.The FEBS journal · 2026Article
- Gold- or Silver-Nanoparticle SERS Platforms for Plasma-Based Diagnostics and AI-Driven Analysis.Sensors (Basel, Switzerland) · 2026Review
- Research on zoledronic acid's synergistic improvement of intervertebral disc degeneration and osteoporosis by regulating inflammation matrix microenvironment.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- scTrimClust: a fast approach to robust scRNA-seq analysis using trimmed cell clusters.Bioinformatics advances · 2026Article
- Artificial intelligence-Driven detection and decision support system for precision management of maize downy mildew.PloS one · 2026Article
- A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology.Scientific reports · 2025Article
- Development and validation of a cranial ultrasound imaging-based deep learning model for periventricular-intraventricular haemorrhage detection and grading: a two-centre study.Pediatric radiology · 2025Observational
- Dissecting Morphological and Functional Dynamics of Non-Tumorigenic and Triple-Negative Breast Cancer Cell Lines Using PCA and t-SNE Analysis.Cancer reports (Hoboken, N.J.) · 2025Article
- Topological Neuroscience: Linking Circuits to Function.Annual review of neuroscience · 2025Review
- Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.Nature communications · 2025Article
- Article
- Dimensionality reduction for visualizing spatially resolved profiling data using SpaSNE.GigaScience · 2025Article
- Enhancing chemical synthesis research with NLP: Word embeddings for chemical reagent identification-A case study on nano-FeCu.iScience · 2024Article
- ImAge quantitates aging and rejuvenation.Nature aging · 2024Article
- Article
- AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment.SSM - population health · 2024Article
- Imaging-based chromatin and epigenetic age, ImAge, quantitates aging and rejuvenation.Research square · 2023Article
- Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy.Frontiers in genetics · 2023Review
- Front-end Weber-Fechner gain control enhances the fidelity of combinatorial odor coding.eLife · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The t-distributed stochastic neighbor embedding (t-SNE) method is one of the leading techniques for data visualization and clustering. This method finds lower-dimensional embedding of data points while minimizing distortions in distances between neighboring data points. By construction, t-SNE discards information about large-scale structure of the data. We show that adding a global cost function to the t-SNE cost function makes it possible to cluster the data while preserving global intercluster data structure. We test the new global t-SNE (g-SNE) method on one synthetic and two real data sets on flower shapes and human brain cells. We find that significant and meaningful global structure exists in both the plant and human brain data sets. In all cases, g-SNE outperforms t-SNE and UMAP in preserving the global structure. Topological analysis of the clustering result makes it possible to find an appropriate trade-off of data distribution across scales. We find differences in how data are distributed across scales between the two subjects that were part of the human brain data set. Thus, by striving to produce both accurate clustering and positioning between clusters, the g-SNE method can identify new aspects of data organization across scales.
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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.