Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Yang, Yongli; Li, Mingli; Zhu, Yixuan; Wang, Xiaoyi; Chen, Qiang; Lu, Shaoxiong

doi:10.1016/j.heliyon.2024.e31311

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e31311

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Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Yongli Yang,

Mingli Li,

Yixuan Zhu

et al.

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Genetic biomarker prediction based on gender disparity in asthma throughout machine learning

Chen,

Yuan,

Meng

et al. 2024

Front. Med.

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BackgroundAsthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1–18% across different nations. Gender differences in asthma prevalence have attracted much attention.PurposeThe aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning.MethodThe data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F1 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 differences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression.ResultIn datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F1 score. Our findings reveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared to males (p < 0.01).ConclusionXIST serves as a genetic biomarker for gender differences in the prevalence of asthma.

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Genetic biomarker prediction based on gender disparity in asthma throughout machine learning

Chen,

Yuan,

Meng

et al. 2024

Front. Med.

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Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning

Cited by 1 publication

References 93 publications

Genetic biomarker prediction based on gender disparity in asthma throughout machine learning

Genetic biomarker prediction based on gender disparity in asthma throughout machine learning

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