ArticlePeerJ2020
Key genes and co-expression modules involved in asthma pathogenesis.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 17 citations in OpenAlex.
- Transcriptomic and physiological responses ofFrontiers in plant science · 2024Article
- Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method.Scientific reports · 2023Article
- Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach.iScience · 2023Article
- Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.Frontiers in genetics · 2023Article
- Establishment and validation of a predictive model of preeclampsia based on transcriptional signatures of 43 genes in decidua basalis and peripheral blood.BMC bioinformatics · 2022Article
- Metabolomic Associations of Asthma in the Hispanic Community Health Study/Study of Latinos.Metabolites · 2022Article
- Network-Based Analysis Reveals Novel Biomarkers in Peripheral Blood of Patients With Preeclampsia.Frontiers in molecular biosciences · 2022Article
- Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset.Artificial intelligence in medicine · 2021Article
- Article
- Redox Role of ROS and Inflammation in Pulmonary Diseases.Advances in experimental medicine and biology · 2021Review
- Stratification of Estrogen Receptor-Negative Breast Cancer Patients by Integrating the Somatic Mutations and Transcriptomic Data.Frontiers in genetics · 2021Article
- Novel Comprehensive Bioinformatics Approaches to Determine the Molecular Genetic Susceptibility Profile of Moderate and Severe Asthma.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and weighted gene co-expression network analysis (WGCNA) have been widely used due to its well-known accuracy in the biological field. However, due to the nature of a gene's multiple functions, it is challenging to locate the exact genes involved in complex diseases such as asthma. In this study, we combined machine learning and WGCNA in order to analyze the gene expression data of asthma for better understanding of associated pathogenesis. Specifically, the role of machine learning is assigned to screen out the key genes in the asthma development, while the role of WGCNA is to set up gene co-expression network. Our results indicated that hormone secretion regulation, airway remodeling, and negative immune regulation, were all regulated by critical gene modules associated with pathogenesis of asthma progression. Overall, the method employed in this study helped identify key genes in asthma and their roles in the asthma pathogenesis.
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What Socratic holds
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.