Robotics & Machine Learning Daily News2024,Issue(Feb.8) :27-28.

Fudan University Obstetrics and Gynecology Hospital Reports Findings in Bronchopulmonary Dysplasia (Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning ...)

Robotics & Machine Learning Daily News2024,Issue(Feb.8) :27-28.

Fudan University Obstetrics and Gynecology Hospital Reports Findings in Bronchopulmonary Dysplasia (Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning ...)

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Abstract

New research on Lung Diseases and Conditions - Bronchopulmonary Dysplasia is the subject of a report. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient.” Our news journalists obtained a quote from the research from Fudan University Obstetrics and Gynecology Hospital, “Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, includ- ing SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD.”

Key words

Shanghai/People's Republic of China/Asia/Algorithms/Biomarkers/Bronchopulmonary Dysplasia/Cyborgs/Dermatology/Diagnostics and Screening/Dysplasia/Emerging Technologies/Genetics/Health and Medicine/Lung Diseases and Conditions/Machine Learning/Pulmonology/Respiratory Tract Diseases and Conditions/Ventilator-Induced Lung Injury

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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