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ORIGINAL RESEARCH article

Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1397746
This article is part of the Research Topic An Overview on Allergic and Pulmonary Diseases: from Birth to Childhood - Volume II View all 5 articles

Genetic Biomarker Prediction Based on Gender Disparity in Asthma throughout Machine Learning

Provisionally accepted
Cai Chen Cai Chen 1Fenglong Yuan Fenglong Yuan 2*Xiangwei Meng Xiangwei Meng 3*Fulai Peng Fulai Peng 4*Xuekun Shao Xuekun Shao 5*Cheng Wang Cheng Wang 6Yang Shen Yang Shen 7*Haitao Du Haitao Du 6*Danyang Lv Danyang Lv 4*Ningling Zhang Ningling Zhang 4*Xiuli Wang Xiuli Wang 2*Tao Wang Tao Wang 8*Ping Wang Ping Wang 6
  • 1 Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China
  • 2 Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, Shandong Province, China
  • 3 Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China, Jinan, Shandong Province, China
  • 4 Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, 250000, China, Jinan, Shandong Province, China
  • 5 School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
  • 6 Shandong Academy of Chinese Medicine, Jinan 250014, China, Jinan, China
  • 7 Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P. R. China, Wuhan, Hebei Province, China
  • 8 Neck, Shoulder, Lumbar and Leg Pain Hospital, Shandong First Medical University, Jinan, Shandong Province, China

The final, formatted version of the article will be published soon.

    Background: Asthma 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.The aim of this study was to investigate biomarkers 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 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.In 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).

    Keywords: Asthma, gender disparity, machine learning, biomarker, Prevalence

    Received: 08 Mar 2024; Accepted: 02 Sep 2024.

    Copyright: © 2024 Chen, Yuan, Meng, Peng, Shao, Wang, Shen, Du, Lv, Zhang, Wang, Wang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Fenglong Yuan, Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, Shandong Province, China
    Xiangwei Meng, Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China, Jinan, Shandong Province, China
    Fulai Peng, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, 250000, China, Jinan, Shandong Province, China
    Xuekun Shao, School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, Shandong Province, China
    Yang Shen, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, P. R. China, Wuhan, Hebei Province, China
    Haitao Du, Shandong Academy of Chinese Medicine, Jinan 250014, China, Jinan, China
    Danyang Lv, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, 250000, China, Jinan, Shandong Province, China
    Ningling Zhang, Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, 250000, China, Jinan, Shandong Province, China
    Xiuli Wang, Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, Shandong Province, China
    Tao Wang, Neck, Shoulder, Lumbar and Leg Pain Hospital, Shandong First Medical University, Jinan, Shandong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.