利用嵌套主动机器学习改进目标质谱数据分析

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS
Duran Bao, Qingbo Shu, Bo Ning, Michael Tang, Yubing Liu, Noel Wong, Zhengming Ding, Zizhan Zheng, Christopher J. Lyon, Tony Hu, Jia Fan
{"title":"利用嵌套主动机器学习改进目标质谱数据分析","authors":"Duran Bao,&nbsp;Qingbo Shu,&nbsp;Bo Ning,&nbsp;Michael Tang,&nbsp;Yubing Liu,&nbsp;Noel Wong,&nbsp;Zhengming Ding,&nbsp;Zizhan Zheng,&nbsp;Christopher J. Lyon,&nbsp;Tony Hu,&nbsp;Jia Fan","doi":"10.1002/aisy.202470035","DOIUrl":null,"url":null,"abstract":"<p><b>Targeted Mass Spectrometry Data Analysis</b>\n </p><p>The application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) has facilitated the earlier detection and diagnosis of diseases preceding the manifestation of symptoms, but data analysis is complicated for clinical application. Integrating an automated machine learning pipeline can optimize LC-MS/MS data processing and analysis, even with limited training datasets. Machine learning pipelines can also implement an active learning nested model to mitigate bias from imbalanced training datasets, providing more accurate clinical proteomic analysis and disease diagnostic results. For more details, refer to article number 2300773 by Jia Fan, Duran Bao, and co-workers.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":null,"pages":null},"PeriodicalIF":6.8000,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470035","citationCount":"0","resultStr":"{\"title\":\"Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning\",\"authors\":\"Duran Bao,&nbsp;Qingbo Shu,&nbsp;Bo Ning,&nbsp;Michael Tang,&nbsp;Yubing Liu,&nbsp;Noel Wong,&nbsp;Zhengming Ding,&nbsp;Zizhan Zheng,&nbsp;Christopher J. Lyon,&nbsp;Tony Hu,&nbsp;Jia Fan\",\"doi\":\"10.1002/aisy.202470035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><b>Targeted Mass Spectrometry Data Analysis</b>\\n </p><p>The application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) has facilitated the earlier detection and diagnosis of diseases preceding the manifestation of symptoms, but data analysis is complicated for clinical application. Integrating an automated machine learning pipeline can optimize LC-MS/MS data processing and analysis, even with limited training datasets. Machine learning pipelines can also implement an active learning nested model to mitigate bias from imbalanced training datasets, providing more accurate clinical proteomic analysis and disease diagnostic results. For more details, refer to article number 2300773 by Jia Fan, Duran Bao, and co-workers.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":93858,\"journal\":{\"name\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2024-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470035\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202470035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/aisy.202470035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

有针对性的质谱数据分析 液相色谱-串联质谱(LC-MS/MS)的应用有助于在症状出现之前更早地检测和诊断疾病,但临床应用中的数据分析非常复杂。整合自动化机器学习管道可以优化 LC-MS/MS 数据处理和分析,即使训练数据集有限。机器学习管道还可以实施主动学习嵌套模型,以减轻不平衡训练数据集带来的偏差,从而提供更准确的临床蛋白质组分析和疾病诊断结果。更多详情,请参阅范佳、鲍杜兰及合作者撰写的文章,文章编号:2300773。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Targeted Mass Spectrometry Data Analysis

The application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) has facilitated the earlier detection and diagnosis of diseases preceding the manifestation of symptoms, but data analysis is complicated for clinical application. Integrating an automated machine learning pipeline can optimize LC-MS/MS data processing and analysis, even with limited training datasets. Machine learning pipelines can also implement an active learning nested model to mitigate bias from imbalanced training datasets, providing more accurate clinical proteomic analysis and disease diagnostic results. For more details, refer to article number 2300773 by Jia Fan, Duran Bao, and co-workers.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
4 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信