Evidence map›Paper›PMID 39963184›Full record

ArticleFrontiers in allergy2025

Machine learning-based screening of asthma biomarkers and related immune infiltration.

Xiaoying Zhong, Jingjing Song, Changyu Lei, Xiaoming Wang, Yufei Wang, Jiahui Yu, Wei Dai, Xinyi Xu, Junwen Fan, Xiaodong Xia and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in allergy, 2025. 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
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.

  1. 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

11 authors.

Xiaoying Zhong *Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jingjing Song *Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Changyu Lei *Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xiaoming WangAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yufei WangAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jiahui YuAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Wei DaiAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xinyi XuAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Junwen FanAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xiaodong XiaAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Weixi ZhangAllergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma. Methods: Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryotic reference transcriptome high-throughput sequencing was performed to identify genes expressed in mouse lung tissues. Results: Thirteen specific asthma genes were obtained from our dataset analysis ( Discussion: This study identified new marker genes for the prediction and diagnosis of asthma, which can be further validated and applied clinically.

Indexed as

asthmadifferentially expressed genesleast absolute shrinkage and selection operator (LASSO) regression modelmachine learningsupport vector machine recursive feature elimination (SVM-RFE)

Identifiers

PMID39963184
PMCPMC11831286

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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.