ArticleiScience2024
Adaptive data embedding for curved spaces.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- A framework for analyzing globaleLife · 2026Article
- Serotonergic modulation of motor subspace dynamics drives a sleep-independent quiescent state.eLife · 2026Article
- Multiscale hyperbolic embedding reveals hierarchical structure in complex biological systems.NPJ systems biology and applications · 2026Article
- Multiscale Hyperbolic Embedding for Cell Hierarchies in Large-Scale Bioinformatics Data.bioRxiv : the preprint server for biology · 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
2 authors.
Funding
Abstract
Recent studies have demonstrated the significance of hyperbolic geometry in uncovering low-dimensional structure within complex hierarchical systems. We developed a Bayesian formulation of multi-dimensional scaling (MDS) for embedding data in hyperbolic spaces that allows for a principled determination of manifold parameters such as curvature and dimension. We show that only a small amount of data are needed to constrain the manifold, the optimization is robust against false minima, and the model is able to correctly discern between Hyperbolic and Euclidean data. Application of the method to COVID sequences revealed that viral evolution leaves the dimensionality of the space unchanged but produces a logarithmic increase in curvature, indicating a constant rate of information acquisition optimized under selective pressures. The algorithm also detected a contraction in curvature after the introduction of vaccines. The ability to discern subtle changes and structural shifts showcases the utility of this approach in understanding complex data dynamics.
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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.