Evidence map›Paper›PMID 38774487›Full record

ArticleJournal of computational science2024

Dory: Computation of persistence diagrams up to dimension two for Vietoris-Rips filtrations of large data sets.

Manu Aggarwal, Vipul Periwal

Abstract read
In one paragraph

Article in Journal of computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Manu AggarwalLaboratory of Biological Modeling, NIDDK, National Institutes of Health, 31 Center Dr, Bethesda, 20892, MD, United States.
Vipul PeriwalLaboratory of Biological Modeling, NIDDK, National Institutes of Health, 31 Center Dr, Bethesda, 20892, MD, United States.

Funding

Computing persistent homology in biological datasetsZIADK075161 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI PERIWAL, VIPUL · 2021 to 2025
$949k
Intramural NIH HHS ZIA DK075161
6 · The paper itself

Abstract

Persistent homology (PH) is an approach to topological data analysis (TDA) that computes multi-scale topologically invariant properties of high-dimensional data that are robust to noise. While PH has revealed useful patterns across various applications, computational requirements have limited applications to small data sets of a few thousand points. We present Dory, an efficient and scalable algorithm that can compute the persistent homology of sparse Vietoris-Rips complexes on larger data sets, up to and including dimension two and over the field

Indexed as

algorithmgenome structurelarge data setsmultiscaleTopological data analysis

Identifiers

PMID38774487
PMCPMC11105796

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.