Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method
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 artific...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42581-5 |
| _version_ | 1797559980976504832 |
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| author | Saeed Pirmoradi Seyed Mahdi Hosseiniyan Khatibi Sepideh Zununi Vahed Hamed Homaei Rad Amir Mahdi Khamaneh Zahra Akbarpour Ensiyeh Seyedrezazadeh Mohammad Teshnehlab Kenneth R. Chapman Khalil Ansarin |
| author_facet | Saeed Pirmoradi Seyed Mahdi Hosseiniyan Khatibi Sepideh Zununi Vahed Hamed Homaei Rad Amir Mahdi Khamaneh Zahra Akbarpour Ensiyeh Seyedrezazadeh Mohammad Teshnehlab Kenneth R. Chapman Khalil Ansarin |
| author_sort | Saeed Pirmoradi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:52:57Z |
| format | Article |
| id | doaj.art-82ea6dac5c264d839c7bec614cf9d0b3 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:52:57Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-82ea6dac5c264d839c7bec614cf9d0b32023-11-20T09:18:10ZengNature PortfolioScientific Reports2045-23222023-09-0113111910.1038/s41598-023-42581-5Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining methodSaeed Pirmoradi0Seyed Mahdi Hosseiniyan Khatibi1Sepideh Zununi Vahed2Hamed Homaei Rad3Amir Mahdi Khamaneh4Zahra Akbarpour5Ensiyeh Seyedrezazadeh6Mohammad Teshnehlab7Kenneth R. Chapman8Khalil Ansarin9Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical SciencesKidney Research Center, Tabriz University of Medical SciencesKidney Research Center, Tabriz University of Medical SciencesRahat Breath and Sleep Research Center, Tabriz University of Medical ScienceFaculty of Advanced Medical Sciences, Tabriz University of Medical SciencesRahat Breath and Sleep Research Center, Tabriz University of Medical ScienceTuberculosis and Lung Disease Research Center, Tabriz University of Medical SciencesDepartment of Electric and Computer Engineering, K.N. Toosi University of TechnologyDivision of Respiratory Medicine, Department of Medicine, University of TorontoRahat Breath and Sleep Research Center, Tabriz University of Medical ScienceAbstract 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.https://doi.org/10.1038/s41598-023-42581-5 |
| spellingShingle | Saeed Pirmoradi Seyed Mahdi Hosseiniyan Khatibi Sepideh Zununi Vahed Hamed Homaei Rad Amir Mahdi Khamaneh Zahra Akbarpour Ensiyeh Seyedrezazadeh Mohammad Teshnehlab Kenneth R. Chapman Khalil Ansarin Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method Scientific Reports |
| title | Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method |
| title_full | Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method |
| title_fullStr | Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method |
| title_full_unstemmed | Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method |
| title_short | Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method |
| title_sort | unraveling the link between ptbp1 and severe asthma through machine learning and association rule mining method |
| url | https://doi.org/10.1038/s41598-023-42581-5 |
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