Evidence map›Paper›PMID 36656029›Full record

ArticleMicrobiology spectrum2023

A Machine Learning Approach Reveals a Microbiota Signature for Infection with Mycobacterium avium subsp.

Sang-Mok Lee, Hong-Tae Park, Seojoung Park, Jun Ho Lee, Danil Kim, Han Sang Yoo, Donghyuk Kim

Open access · goldAbstract read
In one paragraph

Article in Microbiology spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. The Impact ofPathogens (Basel, Switzerland) · 2024
    Article
  9. Article
  10. Article
  11. Sarcoidosis,Microorganisms · 2023
    Review
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

7 authors at 2 institutions in 1 country.

Sang-Mok Lee *School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Hong-Tae Park *Department of Infectious Diseases, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea.
Seojoung ParkSchool of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Jun Ho LeeDepartment of Infectious Diseases, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea.
Danil KimDepartment of Farm Animal Medicine, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea.
Han Sang YooDepartment of Infectious Diseases, College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea.ORCID 0000-0003-0548-0835
Donghyuk KimSchool of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
Seoul National University · KRUlsan National Institute of Science and Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although Mycobacterium avium subsp.

Indexed as

Cattle DiseasesGastrointestinal MicrobiomeMycobacterium avium subsp. paratuberculosisParatuberculosisAnimalsAsymptomatic InfectionsBiomarkersCattleFecesRNA, Ribosomal, 16SBiomarkersRNA, Ribosomal, 16S16S rRNA sequencingfeature selectiongut microbiota signaturemachine learning-based predictive modelMycobacterium avium subsp. paratuberculosis

Identifiers

PMID36656029
PMCPMC9927500
OpenAlexW4317433320

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.