Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
16 09 2023
16 09 2023
Historique:
received:
22
03
2023
accepted:
12
09
2023
medline:
18
9
2023
pubmed:
17
9
2023
entrez:
16
9
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37717070
doi: 10.1038/s41598-023-42581-5
pii: 10.1038/s41598-023-42581-5
pmc: PMC10505163
doi:
Substances chimiques
PTBP1 protein, human
0
Heterogeneous-Nuclear Ribonucleoproteins
0
Polypyrimidine Tract-Binding Protein
139076-35-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
15399Informations de copyright
© 2023. Springer Nature Limited.
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