ArticleScientific reports2023
Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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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.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Study of the mechanism of methyltransferase 3 regulation of ferroptosis in allergic rhinitis and promotion of allergic rhinitis in an m6A-dependent mechanism.Journal of inflammation (London, England) · 2025Article
- Phospho-Proteomic Analysis of Interleukin-13 Signaling in Airway Cells Reveals SRC Family Kinase Involvement in Interleukin-13-Induced Inflammatory Responses.International archives of allergy and immunology · 2025Article
- Identification of key genes for fish adaptation to freshwater and seawater based on attention mechanism.BMC genomics · 2025Article
- Potential asthma biomarkers identified by nontargeted proteomics of extracellular vesicles in exhaled breath condensate.The journal of allergy and clinical immunology. Global · 2025Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy.
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Registered trials
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 Socratic graph.