{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:44:15Z","timestamp":1790696655640,"version":"4.1.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005795","name":"Chang Gung Memorial Hospital, Linkou","doi-asserted-by":"publisher","award":["CMRPG3L0401"],"award-info":[{"award-number":["CMRPG3L0401"]}],"id":[{"id":"10.13039\/501100005795","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005795","name":"Chang Gung Memorial Hospital, Linkou","doi-asserted-by":"publisher","award":["CMRPG3L0431"],"award-info":[{"award-number":["CMRPG3L0431"]}],"id":[{"id":"10.13039\/501100005795","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005795","name":"Chang Gung Memorial Hospital, Linkou","doi-asserted-by":"publisher","award":["CMRPG3L1011"],"award-info":[{"award-number":["CMRPG3L1011"]}],"id":[{"id":"10.13039\/501100005795","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting\n                    <jats:italic>Enterococcus faecium<\/jats:italic>\n                    , which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant\n                    <jats:italic>Enterococcus faecium<\/jats:italic>\n                    (VRE\n                    <jats:italic>fm<\/jats:italic>\n                    ) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VRE\n                    <jats:italic>fm<\/jats:italic>\n                    using a deep learning algorithm.\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2022.821233","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T03:24:40Z","timestamp":1654485880000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":41,"title":["Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach"],"prefix":"10.3389","volume":"13","author":[{"given":"Hsin-Yao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsung-Ting","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Ru","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hung-Ching","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorng-Tzong","family":"Horng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jang-Jih","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Hsin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1089\/mdr.2017.0147","article-title":"Vancomycin-resistant enterococci: a review of antimicrobial resistance mechanisms and perspectives of human and animal health","volume":"24","author":"Ahmed","year":"2018","journal-title":"Microb. Drug Resist."},{"key":"B2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"B3","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"\u201cXGBoost: a scalable tree boosting System\u201d","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2120","DOI":"10.3389\/fmicb.2019.02120","article-title":"Incorporating statistical test and machine intelligence into strain typing of staphylococcus haemolyticus based on matrix-assisted laser desorption ionization-time of flight mass spectrometry","volume":"10","author":"Chung","year":"2019","journal-title":"Front. Microbiol."},{"key":"B5","volume-title":"Performance Standards for Antimicrobial Susceptibility Testing.","year":"2020"},{"key":"B6","doi-asserted-by":"publisher","first-page":"2918","DOI":"10.1128\/JCM.01000-12","article-title":"Use of matrix-assisted laser desorption ionization-time of flight mass spectrometry to identify vancomycin-resistant enterococci and investigate the epidemiology of an outbreak","volume":"50","author":"Griffin","year":"2012","journal-title":"J. Clin. Microbiol."},{"key":"B7","unstructured":"HintonG. E.\n            SrivastavaN.\n            KrizhevskyA.\n            SutskeverI.\n            SalakhutdinovR. R.\n          Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580 [cs]2012"},{"key":"B8","unstructured":"\u201cOnline tracking by learning discriminative saliency map with convolutional neural network\u201d597606\n            HongS.\n            YouT.\n            KwakS.\n            HanB.\n          International Conference on Machine Learning2015"},{"key":"B9","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"B10","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.mimet.2014.02.015","article-title":"Insufficient discriminatory power of MALDI-TOF mass spectrometry for typing of Enterococcus faecium and Staphylococcus aureus isolates","volume":"100","author":"Lasch","year":"2014","journal-title":"J. Microbiol. Method."},{"key":"B11","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"B12","doi-asserted-by":"publisher","first-page":"6157","DOI":"10.1016\/j.csbj.2021.11.004","article-title":"Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study","volume":"19","author":"Mortier","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B13","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.5555\/2627435.2670313","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.18129\/B9.bioc.qvalue","author":"Storey","year":"2020","journal-title":"qvalue: Q-Value Estimation for False Discovery Rate Control"},{"key":"B15","unstructured":"Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks\n            WangH.\n            WangZ.\n            DuM.\n            YangF.\n            ZhangZ.\n            DingS."},{"key":"B16","doi-asserted-by":"publisher","first-page":"2393","DOI":"10.3389\/fmicb.2018.02393","article-title":"Rapid detection of heterogeneous vancomycin-intermediate staphylococcus aureus based on matrix-assisted laser desorption ionization time-of-flight: using a machine learning approach and unbiased validation","volume":"9","author":"Wang","year":"","journal-title":"Front. Microbiol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"e0091321","DOI":"10.1128\/Spectrum.00913-21","article-title":"Clinically applicable system for rapidly predicting enterococcus faecium susceptibility to vancomycin","volume":"9","author":"Wang","year":"2021","journal-title":"Microbiol. Spectr."},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa138","article-title":"A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra","author":"Wang","year":"","journal-title":"Brief. Bioinform"},{"key":"B19","doi-asserted-by":"publisher","first-page":"e0194289","DOI":"10.1371\/journal.pone.0194289","article-title":"A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach","volume":"13","author":"Wang","year":"","journal-title":"PLoS ONE."},{"key":"B20","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1186\/s12859-019-3282-7","article-title":"Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques","volume":"20","author":"Wang","year":"2019","journal-title":"BMC Bioinform."},{"key":"B21","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1255\/ejms.1298","article-title":"Application of matrix-assisted laser desorption ionization time-of-flight mass spectrometry in the screening of vana-positive enterococcus faecium","volume":"20","author":"Wei","year":"2014","journal-title":"Eur. J. Mass Spectrom."},{"key":"B22","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1038\/s41591-021-01619-9","article-title":"Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning","volume":"28","author":"Weis","year":"2022","journal-title":"Nat. Med"},{"key":"B23","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ijmm.2010.06.002","article-title":"MALDI-TOF MS fingerprinting allows for discrimination of major methicillin-resistant Staphylococcus aureus lineages","volume":"301","author":"Wolters","year":"2011","journal-title":"Int. J. Med. Microbiol."}],"container-title":["Frontiers in Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2022.821233\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T12:25:03Z","timestamp":1654863903000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2022.821233\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":23,"alternative-id":["10.3389\/fmicb.2022.821233"],"URL":"https:\/\/doi.org\/10.3389\/fmicb.2022.821233","relation":{},"ISSN":["1664-302X"],"issn-type":[{"value":"1664-302X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"article-number":"821233"}}