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, 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":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, 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\":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}
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.
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