Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-08-18 DOI:10.1002/aisy.202470035

Duran Bao, Qingbo Shu, Bo Ning, Michael Tang, Yubing Liu, Noel Wong, Zhengming Ding, Zizhan Zheng, Christopher J. Lyon, Tony Hu, Jia Fan

{"title":"Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning","authors":"Duran Bao, Qingbo Shu, Bo Ning, Michael Tang, Yubing Liu, Noel Wong, Zhengming Ding, Zizhan Zheng, Christopher J. Lyon, Tony Hu, 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":"6 8","pages":""},"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}

引用次数: 0

Abstract

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.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用嵌套主动机器学习改进目标质谱数据分析

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。

求助全文

约1分钟内获得全文去求助

来源期刊