Evidence map›Paper›PMID 34917684›Full record

ReviewBioMed research international2021

Text Mining and Hub Gene Network Analysis of Endometriosis.

Yinuo Wang, Songbiao Zhu, Chengcheng Liu, Haiteng Deng, Zhenyu Zhang

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Identification ofPharmacogenomics and personalized medicine · 2022
    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

5 authors at 2 institutions in 1 country.

Yinuo WangDepartment of Obstetrics and Gynecology, Beijing Chao-Yang Hospital Affiliated to Capital Medical University, Beijing 100020, China.ORCID https://orcid.org/0000-0001-5515-9519
Songbiao ZhuMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0001-9383-2102
Chengcheng LiuMOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, China.ORCID https://orcid.org/0000-0003-4771-2649
Haiteng DengMOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China.ORCID https://orcid.org/0000-0001-9496-1280
Zhenyu ZhangDepartment of Obstetrics and Gynecology, Beijing Chao-Yang Hospital Affiliated to Capital Medical University, Beijing 100020, China.ORCID https://orcid.org/0000-0002-5794-3400
Tsinghua University · CNCapital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study is aimed at systematically characterizing the endometriosis-associated genes based on text mining and at annotating the functions, pathways, and networks of endometriosis-associated hub genes. We extracted endometriosis-associated abstracts published between 1970 and 2020 from the PubMed database. A neural-named entity recognition and multitype normalization tool for biomedical text mining was used to recognize and normalize the genes and proteins embedded in the abstracts. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted to annotate the functions and pathways of recognized genes. Protein-protein interaction analysis was conducted on the genes significantly cooccurring with endometriosis to identify the endometriosis-associated hub genes. A total of 433 genes were recognized as endometriosis-associated genes (

Indexed as

Data MiningELAV-Like Protein 2EndometriosisFemaleGene OntologyGene Regulatory NetworksHumansProtein Interaction MapsSignal TransductionELAV-Like Protein 2

Identifiers

PMID34917684
PMCPMC8670927
OpenAlexW4200271739

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.