Evidence map›Paper›PMID 34490754›Full record

ArticleJournal of Korean medical science2021

Word Embedding Reveals Cyfra 21-1 as a Biomarker for Chronic Obstructive Pulmonary Disease.

Jeongwon Heo, Da Hye Moon, Yoonki Hong, So Hyeon Bak, Jeeyoung Kim, Joo Hyun Park, Byoung-Doo Oh, Yu-Seop Kim, Woo Jin Kim

Open access · goldAbstract read
In one paragraph

Article in Journal of Korean medical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact, top 85% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Pulmonary infection byFrontiers in cellular and infection microbiology · 2026
    Article
  2. 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

9 authors at 2 institutions in 1 country.

Jeongwon HeoDepartment of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.ORCID https://orcid.org/0000-0001-7694-6730
Da Hye MoonDepartment of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.ORCID https://orcid.org/0000-0001-5602-9572
Yoonki HongDepartment of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.ORCID https://orcid.org/0000-0002-1607-6777
So Hyeon BakDepartment of Radiology, School of Medicine, Kangwon National University, Chuncheon, Korea.ORCID https://orcid.org/0000-0003-1039-7016
Jeeyoung KimDepartment of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.ORCID https://orcid.org/0000-0002-0132-6630
Joo Hyun ParkDepartment of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea.ORCID https://orcid.org/0000-0002-6382-0642
Byoung-Doo OhDepartment of Convergence Software, Hallym University, Chuncheon, Korea.ORCID https://orcid.org/0000-0002-9426-6541
Yu-Seop KimDepartment of Convergence Software, Hallym University, Chuncheon, Korea.ORCID https://orcid.org/0000-0003-1645-4173
Woo Jin KimDepartment of Internal Medicine, Kangwon National University Hospital, Chuncheon, Korea.ORCID https://orcid.org/0000-0003-2927-370X
Kangwon National University · KRHallym University · KR

Funding

Ministry of Science and ICT, South Korea 2019-0-01343
6 · The paper itself

Abstract

backgroundAlthough patients with chronic obstructive pulmonary disease (COPD) experience high morbidity and mortality worldwide, few biomarkers are available for COPD. Here, we analyzed potential biomarkers for the diagnosis of COPD by using word embedding.

methodsTo determine which biomarkers are likely to be associated with COPD, we selected respiratory disease-related biomarkers. Degrees of similarity between the 26 selected biomarkers and COPD were measured by word embedding. And we infer the similarity with COPD through the word embedding model trained in the large-capacity medical corpus, and search for biomarkers with high similarity among them. We used Word2Vec, Canonical Correlation Analysis, and Global Vector for word embedding. We evaluated the associations of selected biomarkers with COPD parameters in a cohort of patients with COPD.

resultsCytokeratin 19 fragment (Cyfra 21-1) was selected because of its high similarity and its significant correlation with the COPD phenotype. Serum Cyfra 21-1 levels were determined in patients with COPD and controls (4.3 ± 5.9 vs. 3.9 ± 3.6 ng/mL,

conclusionWord embedding may be used for the discovery of biomarkers for COPD and Cyfra 21-1 may be used as a biomarker for emphysema. Additional studies are needed to validate Cyfra 21-1 as a biomarker for COPD.

Indexed as

AgedAntigens, NeoplasmBiomarkersBody Mass IndexCanonical Correlation AnalysisCase-Control StudiesCohort StudiesEmphysemaEnzyme-Linked Immunosorbent AssayFemaleHumansKeratin-19MaleMiddle AgedPhenotypePulmonary Disease, Chronic Obstructiveantigen CYFRA21.1Antigens, NeoplasmBiomarkersKeratin-19BiomarkerChronic Obstructive Pulmonary DiseaseCyfra 21-1Word Embedding

Identifiers

PMID34490754
PMCPMC8422037
OpenAlexW3190041983

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.