Evidence map›Paper›PMID 35508272›Full record

ReviewJournal of biomedical informatics2022

Topological data analysis in biomedicine: A review.

Yara Skaf, Reinhard Laubenbacher

Abstract readReview
In one paragraph

Review in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

47 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Topo-CNN: Retinal Image Analysis with Topological Deep Learning.Journal of imaging informatics in medicine · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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.

Yara SkafUniversity of Florida, Department of Mathematics, Gainesville, FL, USA; University of Florida, Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, Gainesville, FL, USA. Electronic address: yara.skaf@ufl.edu.
Reinhard LaubenbacherUniversity of Florida, Department of Mathematics, Gainesville, FL, USA; University of Florida, Department of Medicine, Division of Pulmonary, Critical Care, & Sleep Medicine, Gainesville, FL, USA. Electronic address: reinhard.laubenbacher@medicine.ufl.edu.

Funding

Multiscale modeling of the role of heme during invasive pulmonary aspergillosisR01AI135128 · NIAID · UNIVERSITY OF FLORIDA · PI REINHARD LAUBENBACHER, Borna Mehrad · 2018 to 2026
$4.9M
Together: Transforming and Translating Discovery to Improve HealthTL1TR001428 · NCATS · UNIVERSITY OF FLORIDA · PI MCCORMACK, WAYNE T. · 2015 to 2023
$3.9M
Modular design of multiscale models, with an application to the innate immune response to fungal respiratory pathogens.U01EB024501 · NIBIB · UNIVERSITY OF FLORIDA · PI LAUBENBACHER, REINHARD, MEHRAD, BORNA · 2017 to 2020
$2.9M
Control of heterogeneous microbial communities using model-based multi-objective optimizationR01GM127909 · NIGMS · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI DONGARI-BAGTZOGLOU, ANNA I, LAUBENBACHER, REINHARD · 2018 to 2021
$1.8M
NCATS NIH HHS TL1 TR001428NIAID NIH HHS R01 AI135128NIBIB NIH HHS U01 EB024501NIGMS NIH HHS R01 GM127909
6 · The paper itself

Abstract

Significant technological advances made in recent years have shepherded a dramatic increase in utilization of digital technologies for biomedicine- everything from the widespread use of electronic health records to improved medical imaging capabilities and the rising ubiquity of genomic sequencing contribute to a "digitization" of biomedical research and clinical care. With this shift toward computerized tools comes a dramatic increase in the amount of available data, and current tools for data analysis capable of extracting meaningful knowledge from this wealth of information have yet to catch up. This article seeks to provide an overview of emerging mathematical methods with the potential to improve the abilities of clinicians and researchers to analyze biomedical data, but may be hindered from doing so by a lack of conceptual accessibility and awareness in the life sciences research community. In particular, we focus on topological data analysis (TDA), a set of methods grounded in the mathematical field of algebraic topology that seeks to describe and harness features related to the "shape" of data. We aim to make such techniques more approachable to non-mathematicians by providing a conceptual discussion of their theoretical foundations followed by a survey of their published applications to scientific research. Finally, we discuss the limitations of these methods and suggest potential avenues for future work integrating mathematical tools into clinical care and biomedical informatics.

Indexed as

Data AnalysisDiagnostic ImagingApplied topologyBig data analyticsBiomedical informaticsMachine learningMapperPersistent homologyPersonalized medicineTDATopological data analysis

Identifiers

PMID35508272
PMCPMC13648319

What OpenQuestion holds

Textmetadata
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.