[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125736-en":3,"doc-seo-125736-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125736,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning","MALDI-TOF mass spectrometry (MS) offers rapid, cost-effective microbial identification, but commercial databases can limit accurate differentiation of specific Bifidobacterium subspecies. This study evaluates MALDI-TOF MS protein profiles combined with prediction methods to distinguish Bifidobacterium longum subsp. infantis and B. longum. Analysis of 59 B. longum and 41 B. infantis strains identifies five biomarker peaks and supports machine-learning models, with random forest achieving the best performance (AUC 0.984) and up to 96.67% accuracy using voting on multi-spectra. Results support commercial and quality-control applications in probiotics and pharmaceuticals.","TYPE Original Research PUBLISHED 04 December 2023 DOI 10.3389/fmicb.2023.1297451  \nOPEN ACCESS  \nEDITED BY  \nAldert Zomer,  \nUtrecht University, Netherlands  \nREVIEWED BY  \nAntonella Lupetti, University of Pisa, Italy Sercan Karav,  \nÇanakkale Onsekiz Mart University, Türkiye Miriam Cordovana,  \nBruker Daltonik GmbH, Germany  \n*CORRESPONDENCE  \nJianguo Chen  \n [cij123@126.com](cij123@126.com)[ ](cij123@126.com)Weijie Wang  \n [weijiewang@ncst.edu.cn](weijiewang@ncst.edu.cn)[ ](weijiewang@ncst.edu.cn)Lida Xu  \n [lida.xu@hotgen.com.cn](lida.xu@hotgen.com.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 20 September 2023  \nACCEPTED 16 November 2023  \nPUBLISHED 04 December 2023  \nCITATION  \nLiu K, Wang Y, Zhao M, Xue G, Wang A, Wang W, Xu L and Chen J (2023) Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning.  \nFront. Microbiol. 14:1297451 .  \ndoi: 10.3389/fmicb.2023.1297451  \nCOPYRIGHT  \n© 2023 Liu, Wang, Zhao, Xue, Wang, Wang, Xu and Chen. 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) and the copyright owner(s) 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.  \nRapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning  \nKexin Liu 1, 2†, Yajie Wang3†, Minlei Zhao4†, Gaogao Xue 2, Ailan Wang 2, Weijie Wang 1*, Lida Xu 2* and Jianguo Chen4*  \n1College of Life Science, North China University of Science and Technology, Tangshan, China, 2 Beijing Hotgen Biotechnology Inc., Beijing, China, 3 Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical, Beijing, China, 4 Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China  \nAlthough MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum) . The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE) . To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM) . The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67% . The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.  \nKEYWORDS  \nBifidobacterium longum subspecies, MALDI-TOF MS, machine learning, identification,  \nB. longum, B. infantis  \n1 Introdu","cbCaidpyAIOpOo5V","https://ap.wps.com/l/cbCaidpyAIOpOo5V","pdf",13204745,1,13,"English","en",105,"# Introduction\n## Rationale and biological background\n## Existing identification methods\n# Methods\n## MALDI-TOF MS protein profiling\n## Feature selection and machine-learning models\n# Results\n## Biomarker peak identification\n## Model performance and comparisons\n# Applications and implications","[{\"question\":\"Why are current MALDI-TOF MS databases insufficient for Bifidobacterium subspecies identification?\",\"answer\":\"Commercial MALDI-TOF MS databases have limitations in accurately distinguishing specific Bifidobacterium subspecies, motivating the need for improved differentiation using MALDI-TOF profiles with predictive methods.\"},{\"question\":\"What biomarker peaks were identified for distinguishing B. longum and B. infantis?\",\"answer\":\"Five distinct biomarker peaks were identified at m/z 2929, 4408, 5381, 5394, and 8817 using Recurrent Feature Elimination (RFE).\"},{\"question\":\"Which machine-learning model performed best and what were its key metrics?\",\"answer\":\"Random forest performed best, with an AUC of 0.984, and achieved the highest accuracy of 96.67% when combined with a voting mechanism on multi-mass spectrometry data.\"}]","Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning | 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are current MALDI-TOF MS databases insufficient for Bifidobacterium subspecies identification?","Question",{"text":75,"@type":76},"Commercial MALDI-TOF MS databases have limitations in accurately distinguishing specific Bifidobacterium subspecies, motivating the need for improved differentiation using MALDI-TOF profiles with predictive methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What biomarker peaks were identified for distinguishing B. longum and B. infantis?",{"text":80,"@type":76},"Five distinct biomarker peaks were identified at m/z 2929, 4408, 5381, 5394, and 8817 using Recurrent Feature Elimination (RFE).",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning model performed best and what were its key metrics?",{"text":84,"@type":76},"Random forest performed best, with an AUC of 0.984, and achieved the highest accuracy of 96.67% when combined with a voting mechanism on multi-mass spectrometry 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