首页|Department of Pulmonary and Critical Care Medicine Reports Findings in Asthma (G enetic biomarker prediction based on gender disparity in asthma throughout machi ne learning)
Department of Pulmonary and Critical Care Medicine Reports Findings in Asthma (G enetic biomarker prediction based on gender disparity in asthma throughout machi ne learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Lung Diseases and Cond itions - Asthma is the subject of a report. According to news originating from Y antai, People’s Republic of China, by NewsRx correspondents, research stated, “A sthma is a chronic respiratory condition affecting populations worldwide, with p revalence ranging from 1-18% across different nations. Gender diff erences in asthma prevalence have attracted much attention.” Financial support for this research came from Key Research and Development Plan of Shandong Province. Our news journalists obtained a quote from the research from the Department of P ulmonary and Critical Care Medicine, “The aim of this study was to investigate b iomarkers of gender differences in asthma prevalence based on machine learning. The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males a nd 335 females. Theses samples were divided into male group and female group, re spectively. Grid search and cross-validation were employed to adjust model param eters for support vector machine, random forest, decision tree and logistic regr ession model. Accuracy, precision, recall, and F score were used to evaluate the performance of the models during the training process. After model optimization , four machine learning models were utilized to predict biomarkers of sex differ ences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression. In datasets GSE76262 and GSE69683, suppo rt vector machine, random forest, logistic regression, and decision tree all ach ieve 100% accuracy, precision, recall, and F score. Our findings r eveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared t o males (<0.01).”
YantaiPeople’s Republic of ChinaAsiaAsthmaBiomarkersBronchial Diseases and ConditionsCyborgsDiagnostics an d ScreeningEmerging TechnologiesGeneticsHealth and MedicineImmune System Diseases and ConditionsLung Diseases and ConditionsMachine LearningObstru ctive Lung Diseases and ConditionsRespiratory HypersensitivityRespiratory Tr act Diseases and ConditionsSupport Vector MachinesVector Machines
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