Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.

IF 2.7 3区 医学 Q2 RESPIRATORY SYSTEM International Journal of Chronic Obstructive Pulmonary Disease Pub Date : 2024-11-05 eCollection Date: 2024-01-01 DOI:10.2147/COPD.S478634
Zehua Yang, Yamei Zheng, Lei Zhang, Jie Zhao, Wenya Xu, Haihong Wu, Tian Xie, Yipeng Ding
{"title":"Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.","authors":"Zehua Yang, Yamei Zheng, Lei Zhang, Jie Zhao, Wenya Xu, Haihong Wu, Tian Xie, Yipeng Ding","doi":"10.2147/COPD.S478634","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD.</p><p><strong>Methods: </strong>Healthy subjects (n=290) and COPD patients (n=233) were included in this study, the Agena MassARRAY platform was applied to genotype the subjects for SNPs. The selected sample loci were first screened by logistic regression analysis, based on which the key SNPs were further screened by LASSO regression, RFE algorithm and Random Forest algorithm, and the ROC curves were plotted to assess the discriminative performance of the models to screen the best prediction model. Finally, the best prediction model was used for the prediction of risk factors for COPD.</p><p><strong>Results: </strong>One-way logistic regression analysis screened 44 candidate SNPs from 146 SNPs, on the basis of which 44 SNPs were screened or feature ranked using LASSO model, RFE-Caret, RFE-Lda, RFE-lr, RFE-nb, RFE-rf, RFE-treebag algorithms and random forest model, respectively, and obtained ROC curve values of 0.809, 0.769, 0.798, 0.743, 0.686, 0.766, 0.743, 0.719, respectively, so we selected the lasso model as the best model, and then constructed a column-line graph model for the 25 SNPs screened in it, and found that rs12479210 might be the potential risk factors for COPD.</p><p><strong>Conclusion: </strong>The LASSO model is the best predictive model for COPD and rs12479210 may be a potential risk locus for COPD.</p>","PeriodicalId":48818,"journal":{"name":"International Journal of Chronic Obstructive Pulmonary Disease","volume":"19 ","pages":"2397-2414"},"PeriodicalIF":2.7000,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11549878/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Chronic Obstructive Pulmonary Disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/COPD.S478634","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0

Abstract

Background and purpose: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD.

Methods: Healthy subjects (n=290) and COPD patients (n=233) were included in this study, the Agena MassARRAY platform was applied to genotype the subjects for SNPs. The selected sample loci were first screened by logistic regression analysis, based on which the key SNPs were further screened by LASSO regression, RFE algorithm and Random Forest algorithm, and the ROC curves were plotted to assess the discriminative performance of the models to screen the best prediction model. Finally, the best prediction model was used for the prediction of risk factors for COPD.

Results: One-way logistic regression analysis screened 44 candidate SNPs from 146 SNPs, on the basis of which 44 SNPs were screened or feature ranked using LASSO model, RFE-Caret, RFE-Lda, RFE-lr, RFE-nb, RFE-rf, RFE-treebag algorithms and random forest model, respectively, and obtained ROC curve values of 0.809, 0.769, 0.798, 0.743, 0.686, 0.766, 0.743, 0.719, respectively, so we selected the lasso model as the best model, and then constructed a column-line graph model for the 25 SNPs screened in it, and found that rs12479210 might be the potential risk factors for COPD.

Conclusion: The LASSO model is the best predictive model for COPD and rs12479210 may be a potential risk locus for COPD.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于机器学习算法筛选慢性阻塞性肺病 (COPD) 的最佳风险模型和易感 SNPs。
背景和目的:慢性阻塞性肺病(COPD)是一种常见的进行性疾病,受遗传和环境因素的影响,遗传因素是慢性阻塞性肺病的重要决定因素。本研究的重点是筛选评估 COPD 相关 SNP 的最佳预测模型,然后利用最佳模型预测 COPD 的潜在风险因素。方法:本研究纳入健康受试者(n=290)和 COPD 患者(n=233),应用 Agena MassARRAY 平台对受试者进行 SNP 基因分型。首先通过逻辑回归分析筛选出所选的样本位点,在此基础上通过 LASSO 回归、RFE 算法和随机森林算法进一步筛选出关键 SNPs,并绘制 ROC 曲线以评估模型的判别性能,从而筛选出最佳预测模型。最后,利用最佳预测模型预测慢性阻塞性肺病的风险因素:结果:单向逻辑回归分析从 146 个 SNPs 中筛选出 44 个候选 SNPs,在此基础上分别使用 LASSO 模型、RFE-Caret、RFE-Lda、RFE-lr、RFE-nb、RFE-rf、RFE-treebag 算法和随机森林模型对 44 个 SNPs 进行筛选或特征排序,得到的 ROC 曲线值分别为 0.分别为0.809、0.769、0.798、0.743、0.686、0.766、0.743、0.719,因此选择lasso模型为最佳模型,然后对其中筛选出的25个SNPs构建柱状线图模型,发现rs12479210可能是COPD的潜在危险因素:结论:LASSO模型是慢性阻塞性肺病的最佳预测模型,rs12479210可能是慢性阻塞性肺病的潜在风险位点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.80
自引率
10.70%
发文量
372
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed journal of therapeutics and pharmacology focusing on concise rapid reporting of clinical studies and reviews in COPD. Special focus will be given to the pathophysiological processes underlying the disease, intervention programs, patient focused education, and self management protocols. This journal is directed at specialists and healthcare professionals
期刊最新文献
Clinical Characteristics and Outcomes of Hospitalized AECOPDs Secondary to SARS-CoV-2 versus Other Respiratory Viruses. Stability of Neutrophil to Lymphocyte Ratio in Acute Exacerbation of Chronic Obstructive Pulmonary Disease and Its Relationship with Clinical Outcomes: A Retrospective Cohort Study. Noninvasive Positive Pressure Ventilation versus High-Flow Nasal Cannula for Chronic Obstructive Pulmonary Disease: An Updated Narrative Review. Respiratory Specialist Visits Before Admissions with COPD Exacerbation are Linked to Improved Management and Outcomes. Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1