{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T15:14:45Z","timestamp":1791645285615,"version":"4.3.4"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Heilongjiang Provincial Natural Science Foundation Joint Guidance Project","award":["LH2024H055"],"award-info":[{"award-number":["LH2024H055"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82474215"],"award-info":[{"award-number":["82474215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Anal Bioanal Chem"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00216-025-06260-w","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T05:34:04Z","timestamp":1765604044000},"page":"1829-1844","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Advances in the application of artificial intelligence in mass spectrometry-based analysis of traditional Chinese medicine: compound identification and metabolic pathway elucidation"],"prefix":"10.1007","volume":"418","author":[{"given":"Jiaqi","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lincheng","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peiliang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"issue":"8","key":"6260_CR1","doi-asserted-by":"publisher","first-page":"7415","DOI":"10.1021\/acs.chemrev.1c00377","volume":"122","author":"MF Jarrold","year":"2022","unstructured":"Jarrold MF. Applications of charge detection mass spectrometry in molecular biology and biotechnology. Chem Rev. 2022;122(8):7415\u201341.","journal-title":"Chem Rev"},{"key":"6260_CR2","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.clinbiochem.2020.03.004","volume":"82","author":"C Seger","year":"2020","unstructured":"Seger C, Salzmann L. After another decade: LC-MS\/MS became routine in clinical diagnostics. Clin Biochem. 2020;82:2\u201311.","journal-title":"Clin Biochem"},{"key":"6260_CR3","doi-asserted-by":"publisher","first-page":"3837270","DOI":"10.1155\/2016\/3837270","volume":"2016","author":"B Pang","year":"2016","unstructured":"Pang B, Zhu Y, Lu L, Gu F, Chen H. The applications and features of liquid chromatography-mass spectrometry in the analysis of traditional Chinese medicine. Evid Based Complement Alternat Med. 2016;2016:3837270.","journal-title":"Evid Based Complement Alternat Med"},{"issue":"20","key":"6260_CR4","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","volume":"132","author":"RC Deo","year":"2015","unstructured":"Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920\u201330.","journal-title":"Circulation"},{"key":"6260_CR5","doi-asserted-by":"publisher","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","volume":"79","author":"BHM van der Velden","year":"2022","unstructured":"van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022;79:102470.","journal-title":"Med Image Anal"},{"issue":"4","key":"6260_CR6","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1002\/cac2.12012","volume":"40","author":"Y Jiang","year":"2020","unstructured":"Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun. 2020;40(4):154\u201366.","journal-title":"Cancer Commun"},{"key":"6260_CR7","doi-asserted-by":"publisher","first-page":"464582","DOI":"10.1016\/j.chroma.2023.464582","volume":"1714","author":"X Zhao","year":"2024","unstructured":"Zhao X, Aridi R, Hume J, Subbiah S, Wu X, Chung H, et al. Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs. J Chromatogr A. 2024;1714:464582.","journal-title":"J Chromatogr A"},{"issue":"1","key":"6260_CR8","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1186\/s12934-019-1171-4","volume":"18","author":"S Noda","year":"2019","unstructured":"Noda S, Mori Y, Oyama S, Kondo A, Araki M, Shirai T. Reconstruction of metabolic pathway for isobutanol production in Escherichia coli. Microb Cell Fact. 2019;18(1):124.","journal-title":"Microb Cell Fact"},{"issue":"16","key":"6260_CR9","doi-asserted-by":"publisher","first-page":"6533","DOI":"10.1021\/acs.analchem.2c05079","volume":"95","author":"H Wang","year":"2023","unstructured":"Wang H, Yin Y, Zhu ZJ. Encoding LC-MS-based untargeted metabolomics data into images toward AI-based clinical diagnosis. Anal Chem. 2023;95(16):6533\u201341.","journal-title":"Anal Chem"},{"key":"6260_CR10","doi-asserted-by":"publisher","first-page":"106114","DOI":"10.1016\/j.neunet.2024.106114","volume":"172","author":"M Yasin","year":"2024","unstructured":"Yasin M, Sar\u0131g\u00fcl M, Avci M. Logarithmic learning differential convolutional neural network. Neural Netw. 2024;172:106114.","journal-title":"Neural Netw"},{"key":"6260_CR11","first-page":"1","volume":"2023","author":"HYK Chau","year":"2023","unstructured":"Chau HYK, Ao H, Zhang X, Gao S, Varghese RS, Ressom HW. Deep Learning Based Metabolite Annotation. Annu Int Conf IEEE Eng Med Biol Soc. 2023;2023:1\u20134.","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"6260_CR12","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac759","author":"M R\u00e9au","year":"2023","unstructured":"R\u00e9au M, Renaud N, Xue LC, Bonvin A. Deeprank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. Bioinformatics. 2023. https:\/\/doi.org\/10.1093\/bioinformatics\/btac759.","journal-title":"Bioinformatics"},{"key":"6260_CR13","doi-asserted-by":"publisher","first-page":"462536","DOI":"10.1016\/j.chroma.2021.462536","volume":"1656","author":"Q Yang","year":"2021","unstructured":"Yang Q, Ji H, Fan X, Zhang Z, Lu H. Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning. J Chromatogr A. 2021;1656:462536.","journal-title":"J Chromatogr A"},{"issue":"5","key":"6260_CR14","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1002\/jssc.202001087","volume":"44","author":"SL Zhao","year":"2021","unstructured":"Zhao SL, Fu F, Jia YR, Yu XA, Yu BY, Li RS. Quality assessment and traceability study of Angelicae Sinensis Radix via binary chromatography, triple quadrupole tandem mass spectrometry, and multivariate statistical analysis. J Sep Sci. 2021;44(5):1062\u201371.","journal-title":"J Sep Sci"},{"issue":"2","key":"6260_CR15","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2174\/1389200223666220211093548","volume":"23","author":"X Bai","year":"2022","unstructured":"Bai X, Zhu C, Chen J, Jiang X, Jin Y, Shen R, et al. Recent progress on mass spectrum based approaches for absorption, distribution, metabolism, and excretion characterization of traditional Chinese medicine. Curr Drug Metab. 2022;23(2):99\u2013112.","journal-title":"Curr Drug Metab"},{"issue":"6","key":"6260_CR16","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1007\/s11596-021-2474-3","volume":"41","author":"PR Liu","year":"2021","unstructured":"Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of artificial intelligence in medicine: an overview. Curr Med Sci. 2021;41(6):1105\u201315.","journal-title":"Curr Med Sci"},{"issue":"7","key":"6260_CR17","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1038\/s41592-021-01116-4","volume":"18","author":"L de Perez Souza","year":"2021","unstructured":"de Perez Souza L, Alseekh S, Scossa F, Fernie AR. Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nat Methods. 2021;18(7):733\u201346.","journal-title":"Nat Methods"},{"issue":"5","key":"6260_CR18","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1080\/10408347.2017.1320215","volume":"47","author":"T Furuhashi","year":"2017","unstructured":"Furuhashi T, Okuda K. Application of GC\/MS soft ionization for isomeric biological compound analysis. Crit Rev Anal Chem. 2017;47(5):438\u201353.","journal-title":"Crit Rev Anal Chem"},{"issue":"8","key":"6260_CR19","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1080\/17576180.2025.2490468","volume":"17","author":"K Ieki","year":"2025","unstructured":"Ieki K, Fukuda S, Miyawaki S, Hirowatari K. Regulated bioanalysis of antibody-drug conjugates using LC-MS. Bioanalysis. 2025;17(8):549\u201360.","journal-title":"Bioanalysis"},{"issue":"2","key":"6260_CR20","doi-asserted-by":"publisher","first-page":"93","DOI":"10.2165\/00003088-199223020-00003","volume":"23","author":"YJ Sue","year":"1992","unstructured":"Sue YJ, Shannon M. Pharmacokinetics of drugs in overdose. Clin Pharmacokinet. 1992;23(2):93\u2013105.","journal-title":"Clin Pharmacokinet"},{"key":"6260_CR21","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/978-1-0716-2966-6_9","volume":"2625","author":"MA Saleem","year":"2023","unstructured":"Saleem MA, Mueller A, Bhattacharya SK. Lipidomics analysis with triple quadrupole mass spectrometry. Methods Mol Biol. 2023;2625:103\u20136.","journal-title":"Methods Mol Biol"},{"issue":"2","key":"6260_CR22","first-page":"14","volume":"9","author":"RY Choi","year":"2020","unstructured":"Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol. 2020;9(2):14.","journal-title":"Transl Vis Sci Technol"},{"issue":"4","key":"6260_CR23","doi-asserted-by":"publisher","first-page":"6244","DOI":"10.1109\/TNNLS.2024.3395148","volume":"36","author":"Z Que","year":"2025","unstructured":"Que Z, Lin CJ. One-class SVM probabilistic outputs. IEEE Trans Neural Netw Learn Syst. 2025;36(4):6244\u201356.","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"6260_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/biom14111348","author":"A Bhattarai","year":"2024","unstructured":"Bhattarai A, Meyer J, Petersilie L, Shah SI, Neu LA, Rose CR, et al. Deep-learning-based segmentation of cells and analysis (DL-SCAN). Biomolecules. 2024. https:\/\/doi.org\/10.3390\/biom14111348.","journal-title":"Biomolecules"},{"key":"6260_CR25","doi-asserted-by":"publisher","first-page":"1431862","DOI":"10.3389\/fphar.2024.1431862","volume":"15","author":"M Zhao","year":"2024","unstructured":"Zhao M, Che Y, Gao Y, Zhang X. Application of multi-omics in the study of traditional Chinese medicine. Front Pharmacol. 2024;15:1431862.","journal-title":"Front Pharmacol"},{"issue":"1","key":"6260_CR26","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1038\/s41467-024-54871-1","volume":"16","author":"H Gao","year":"2025","unstructured":"Gao H, Zhu Y, Wang D, Nie Z, Wang H, Wang G, et al. iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control. Nat Commun. 2025;16(1):892.","journal-title":"Nat Commun"},{"key":"6260_CR27","doi-asserted-by":"publisher","unstructured":"Liu X, Yang H, Ai C, Ding Y, Guo F, Tang J. MVML-MPI: Multi-view multi-label learning for metabolic pathway inference. Brief Bioinform. 2023;24(6):bbad393. https:\/\/doi.org\/10.1093\/bib\/bbad393","DOI":"10.1093\/bib\/bbad393"},{"key":"6260_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17050714","author":"A Gschwind","year":"2025","unstructured":"Gschwind A, Ossowski S. AI model for predicting anti-PD1 response in melanoma using multi-omics biomarkers. Cancers (Basel). 2025. https:\/\/doi.org\/10.3390\/cancers17050714.","journal-title":"Cancers (Basel)"},{"issue":"10","key":"6260_CR29","first-page":"900","volume":"22","author":"S Chen","year":"2024","unstructured":"Chen S, Dai B, Zhang D, Yang Y, Zhang H, Zhang J, et al. Advances in intelligent mass spectrometry data processing technology for in vivo analysis of natural medicines. Chin J Nat Med. 2024;22(10):900\u201313.","journal-title":"Chin J Nat Med"},{"issue":"34","key":"6260_CR30","doi-asserted-by":"publisher","first-page":"11692","DOI":"10.1021\/acs.analchem.1c01465","volume":"93","author":"F Wang","year":"2021","unstructured":"Wang F, Liigand J, Tian S, Arndt D, Greiner R, Wishart DS. CFM-ID 4.0: more accurate ESI-MS\/MS spectral prediction and compound identification. Anal Chem. 2021;93(34):11692\u2013700.","journal-title":"Anal Chem"},{"issue":"11","key":"6260_CR31","first-page":"2989","volume":"48","author":"WD Hu","year":"2023","unstructured":"Hu WD, Wang SY, Xu AC, Wang XY, Gao XM, Yang WZ. Characterization and identification of chemical components in traditional Chinese medicine Psoraleae Fructus based on UHPLC-Q-TOF-MS. Zhongguo Zhong Yao Za Zhi. 2023;48(11):2989\u201399.","journal-title":"Zhongguo Zhong Yao Za Zhi"},{"issue":"5","key":"6260_CR32","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1002\/jssc.202000952","volume":"44","author":"Q Wang","year":"2021","unstructured":"Wang Q, Zou Z, Zhang Y, Lin P, Lan T, Qin Z, et al. Characterization of chemical profile and quantification of major representative components of Wendan decoction, a classical traditional Chinese medicine formula. J Sep Sci. 2021;44(5):1036\u201361.","journal-title":"J Sep Sci"},{"key":"6260_CR33","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.aca.2019.01.002","volume":"1058","author":"X Zhang","year":"2019","unstructured":"Zhang X, Lin T, Xu J, Luo X, Ying Y. Deepspectra: an end-to-end deep learning approach for quantitative spectral analysis. Anal Chim Acta. 2019;1058:48\u201357.","journal-title":"Anal Chim Acta"},{"key":"6260_CR34","doi-asserted-by":"publisher","DOI":"10.3390\/biom11121793","author":"AD Shrivastava","year":"2021","unstructured":"Shrivastava AD, Swainston N, Samanta S, Roberts I, Wright Muelas M, Kell DB. Massgenie: a transformer-based deep learning method for identifying small molecules from their mass spectra. Biomolecules. 2021. https:\/\/doi.org\/10.3390\/biom11121793.","journal-title":"Biomolecules"},{"issue":"4-5","key":"6260_CR35","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s10529-019-02665-7","volume":"41","author":"YF Bi","year":"2019","unstructured":"Bi YF, Wang XZ, Jiang S, Liu JS, Zheng MZ, Chen P. Enzymatic transformation of ginsenosides Re, Rg1, and Rf to ginsenosides Rg2 and aglycon PPT by using \u03b2-glucosidase from Thermotoga neapolitana. Biotechnol Lett. 2019;41(4\u20135):613\u201323.","journal-title":"Biotechnol Lett"},{"issue":"34","key":"6260_CR36","doi-asserted-by":"publisher","first-page":"e2203339","DOI":"10.1002\/advs.202203339","volume":"9","author":"H Hu","year":"2022","unstructured":"Hu H, Laskin J. Emerging computational methods in mass spectrometry imaging. Adv Sci. 2022;9(34):e2203339.","journal-title":"Adv Sci"},{"issue":"6","key":"6260_CR37","doi-asserted-by":"publisher","first-page":"585","DOI":"10.3724\/SP.J.1123.2025.01019","volume":"43","author":"C Yang","year":"2025","unstructured":"Yang C, Zhang A, Gao ZQ, Su GY. A review and research prospects on the application of the XCMS mass-spectrometry data-processing software in the environmental science field. Chin J Chromatogr. 2025;43(6):585\u201393.","journal-title":"Chin J Chromatogr"},{"key":"6260_CR38","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-1-0716-4534-5_7","volume":"2925","author":"M Coronado","year":"2025","unstructured":"Coronado M, Pham A, Bhattacharya SK. Comparison of untargeted LC-MS\/MS metabolomics data processing through Compound Discoverer and MS-DIAL. Methods Mol Biol. 2025;2925:113\u201320.","journal-title":"Methods Mol Biol"},{"key":"6260_CR39","doi-asserted-by":"publisher","first-page":"124427","DOI":"10.1016\/j.jchromb.2024.124427","volume":"1251","author":"S Zhou","year":"2025","unstructured":"Zhou S, Xia Y, Hao L, Gao Y, Zhang C. Optimization of liquid chromatography and mass spectrometry parameters based on LC-QQQ: a case study on lysinoalanine. J Chromatogr B Analyt Technol Biomed Life Sci. 2025;1251:124427.","journal-title":"J Chromatogr B Analyt Technol Biomed Life Sci"},{"issue":"11","key":"6260_CR40","doi-asserted-by":"publisher","first-page":"7515","DOI":"10.1021\/acs.analchem.9b05765","volume":"92","author":"P Bonini","year":"2020","unstructured":"Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O. Retip: retention time prediction for compound annotation in untargeted metabolomics. Anal Chem. 2020;92(11):7515\u201322.","journal-title":"Anal Chem"},{"key":"6260_CR41","doi-asserted-by":"publisher","first-page":"154273","DOI":"10.1016\/j.phymed.2022.154273","volume":"106","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Li S, Yang P, Zhang Q, Xu H, Bi K, et al. A multidimensional strategy for uncovering comprehensive quality markers of Schisandra chinensis (Turcz.) Baill based on pharmacodynamics and chemical properties. Phytomedicine. 2022;106:154273.","journal-title":"Phytomedicine"},{"issue":"2","key":"6260_CR42","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1021\/ci600289v","volume":"47","author":"TM Ehrman","year":"2007","unstructured":"Ehrman TM, Barlow DJ, Hylands PJ. Virtual screening of Chinese herbs with Random Forest. J Chem Inf Model. 2007;47(2):264\u201378.","journal-title":"J Chem Inf Model"},{"issue":"1","key":"6260_CR43","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13321-021-00558-4","volume":"13","author":"F Huber","year":"2021","unstructured":"Huber F, van der Burg S, van der Hooft JJJ, Ridder L. MS2Deepscore: a novel deep learning similarity measure to compare tandem mass spectra. J Cheminform. 2021;13(1):84.","journal-title":"J Cheminform"},{"key":"6260_CR44","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph192215348","author":"Y Tan","year":"2022","unstructured":"Tan Y, Zeng Z, Liang H, Weng X, Yao H, Fu Y, et al. Association between perfluoroalkyl and polyfluoroalkyl substances and women\u2019s infertility, NHANES 2013-2016. Int J Environ Res Public Health. 2022. https:\/\/doi.org\/10.3390\/ijerph192215348.","journal-title":"Int J Environ Res Public Health"},{"issue":"6","key":"6260_CR45","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s43588-022-00266-5","volume":"2","author":"J Li","year":"2022","unstructured":"Li J, Chen S, Pan X, Yuan Y, Shen HB. Cell clustering for spatial transcriptomics data with graph neural networks. Nat Comput Sci. 2022;2(6):399\u2013408.","journal-title":"Nat Comput Sci"},{"issue":"3","key":"6260_CR46","first-page":"479","volume":"42","author":"WU Jiashuo","year":"2022","unstructured":"Jiashuo WU, Fangqing Z, Zhuangzhuang LI, Weiyi J, Yue S. Integration strategy of network pharmacology in Traditional Chinese Medicine: a narrative review. J Tradit Chin Med. 2022;42(3):479\u201386.","journal-title":"J Tradit Chin Med"},{"issue":"1","key":"6260_CR47","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1186\/s12872-025-04694-9","volume":"25","author":"Y Qi","year":"2025","unstructured":"Qi Y, Li Y, Wei X, Wu H, Li G, Chen J, et al. Proteomic evaluation of the thrombosis-inflammation interplay in STEMI with MVO. BMC Cardiovasc Disord. 2025;25(1):255.","journal-title":"BMC Cardiovasc Disord"},{"issue":"4","key":"6260_CR48","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1097\/NNE.0000000000001851","volume":"50","author":"MMK Chan","year":"2025","unstructured":"Chan MMK, Wan AWH, Cheung DSK, Choi EPH, Chan EA, Yorke J, et al. Integration of Artificial Intelligence in nursing simulation education: a scoping review. Nurse Educ. 2025;50(4):195\u2013200.","journal-title":"Nurse Educ"},{"key":"6260_CR49","doi-asserted-by":"publisher","first-page":"113652","DOI":"10.1016\/j.jpba.2020.113652","volume":"192","author":"WJ He","year":"2021","unstructured":"He WJ, Cao DM, Chen YB, Shi JJ, Hu T, Zhang ZT, et al. Explore of the beneficial effects of Huang-Lian-Jie-Du Decoction on diabetic encephalopathy in db\/db mice by UPLC-Q-Orbitrap HRMS\/MS based untargeted metabolomics analysis. J Pharm Biomed Anal. 2021;192:113652.","journal-title":"J Pharm Biomed Anal"},{"key":"6260_CR50","doi-asserted-by":"publisher","first-page":"283","DOI":"10.3389\/fphar.2019.00283","volume":"10","author":"S Polo","year":"2019","unstructured":"Polo S, D\u00edaz AF, Gallardo N, Le\u00e1nez S, Balboni G, Pol O. Treatment with the Delta opioid agonist UFP-512 alleviates chronic inflammatory and neuropathic pain: mechanisms implicated. Front Pharmacol. 2019;10:283.","journal-title":"Front Pharmacol"},{"key":"6260_CR51","doi-asserted-by":"publisher","first-page":"106827","DOI":"10.1016\/j.mimet.2023.106827","volume":"213","author":"A Gao","year":"2023","unstructured":"Gao A, Fischer-Jenssen J, Slavic D, Rutherford K, Lippert S, Wilson E, et al. Rapid identification of Salmonella serovars Enteritidis and Typhimurium using whole cell matrix assisted laser desorption ionization - Time of flight mass spectrometry (MALDI-TOF MS) coupled with multivariate analysis and artificial intelligence. J Microbiol Methods. 2023;213:106827.","journal-title":"J Microbiol Methods"},{"issue":"1","key":"6260_CR52","doi-asserted-by":"publisher","first-page":"5405","DOI":"10.1038\/s41467-024-48711-5","volume":"15","author":"H Webel","year":"2024","unstructured":"Webel H, Niu L, Nielsen AB, Locard-Paulet M, Mann M, Jensen LJ, et al. Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning. Nat Commun. 2024;15(1):5405.","journal-title":"Nat Commun"},{"issue":"1","key":"6260_CR53","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41587-019-0375-9","volume":"38","author":"M Wang","year":"2020","unstructured":"Wang M, Jarmusch AK, Vargas F, Aksenov AA, Gauglitz JM, Weldon K, et al. Mass spectrometry searches using MASST. Nat Biotechnol. 2020;38(1):23\u20136.","journal-title":"Nat Biotechnol"},{"key":"6260_CR54","doi-asserted-by":"publisher","first-page":"102305","DOI":"10.1016\/j.cbpa.2023.102305","volume":"74","author":"Z Liu","year":"2023","unstructured":"Liu Z, Chen X, Yang S, Tian R, Wang F. Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization. Curr Opin Chem Biol. 2023;74:102305.","journal-title":"Curr Opin Chem Biol"},{"key":"6260_CR55","doi-asserted-by":"publisher","first-page":"2862458","DOI":"10.1155\/2018\/2862458","volume":"2018","author":"Z Fan","year":"2018","unstructured":"Fan Z, Kong F, Zhou Y, Chen Y, Dai Y. Intelligence algorithms for protein classification by mass spectrometry. Biomed Res Int. 2018;2018:2862458.","journal-title":"Biomed Res Int"},{"issue":"12","key":"6260_CR56","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1016\/j.molmed.2024.07.009","volume":"30","author":"S Sarkar","year":"2024","unstructured":"Sarkar S, Zheng X, Clair GC, Kwon YM, You Y, Swensen AC, et al. Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements. Trends Mol Med. 2024;30(12):1137\u201351.","journal-title":"Trends Mol Med"},{"issue":"19","key":"6260_CR57","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.1016\/j.jmb.2018.05.023","volume":"430","author":"AM Daulat","year":"2018","unstructured":"Daulat AM, Puvirajesinghe TM, Camoin L, Borg JP. Mapping cellular polarity networks using mass spectrometry-based strategies. J Mol Biol. 2018;430(19):3545\u201364.","journal-title":"J Mol Biol"},{"issue":"4","key":"6260_CR58","doi-asserted-by":"publisher","first-page":"e23400","DOI":"10.1002\/bip.23400","volume":"112","author":"W Gardner","year":"2021","unstructured":"Gardner W, Cutts SM, Phillips DR, Pigram PJ. Understanding mass spectrometry images: complexity to clarity with machine learning. Biopolymers. 2021;112(4):e23400.","journal-title":"Biopolymers"},{"issue":"3","key":"6260_CR59","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1002\/mas.21602","volume":"39","author":"N Verbeeck","year":"2020","unstructured":"Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. Mass Spectrom Rev. 2020;39(3):245\u201391.","journal-title":"Mass Spectrom Rev"},{"issue":"11","key":"6260_CR60","doi-asserted-by":"publisher","first-page":"4240","DOI":"10.7150\/thno.94950","volume":"14","author":"Y Liu","year":"2024","unstructured":"Liu Y, Ji Y, Chen J, Zhang Y, Li X, Li X. Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform. Theranostics. 2024;14(11):4240\u201355.","journal-title":"Theranostics"},{"issue":"29","key":"6260_CR61","doi-asserted-by":"publisher","first-page":"10879","DOI":"10.1021\/acs.analchem.2c05785","volume":"95","author":"D Li","year":"2023","unstructured":"Li D, Qian Y, Yao H, Yu W, Ma X. DeepS: accelerating 3D mass spectrometry imaging via a deep neural network. Anal Chem. 2023;95(29):10879\u201386.","journal-title":"Anal Chem"},{"issue":"7","key":"6260_CR62","doi-asserted-by":"publisher","first-page":"2246","DOI":"10.1021\/acs.jproteome.2c00807","volume":"22","author":"LY Geer","year":"2023","unstructured":"Geer LY, Lapin J, Slotta DJ, Mak TD, Stein SE. AIomics: exploring more of the proteome using mass spectral libraries extended by artificial intelligence. J Proteome Res. 2023;22(7):2246\u201355.","journal-title":"J Proteome Res"},{"key":"6260_CR63","doi-asserted-by":"publisher","DOI":"10.3390\/ijms231911269","author":"E Barberis","year":"2022","unstructured":"Barberis E, Khoso S, Sica A, Falasca M, Gennari A, Dondero F, et al. Precision medicine approaches with metabolomics and artificial intelligence. Int J Mol Sci. 2022. https:\/\/doi.org\/10.3390\/ijms231911269.","journal-title":"Int J Mol Sci"},{"issue":"9","key":"6260_CR64","doi-asserted-by":"publisher","first-page":"4728","DOI":"10.1021\/acs.analchem.4c06750","volume":"97","author":"CS Movassaghi","year":"2025","unstructured":"Movassaghi CS, Sun J, Jiang Y, Turner N, Chang V, Chung N, et al. Recent advances in mass spectrometry-based bottom-up proteomics. Anal Chem. 2025;97(9):4728\u201349.","journal-title":"Anal Chem"},{"issue":"1","key":"6260_CR65","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.cmet.2021.11.005","volume":"34","author":"M Giera","year":"2022","unstructured":"Giera M, Yanes O, Siuzdak G. Metabolite discovery: biochemistry\u2019s scientific driver. Cell Metab. 2022;34(1):21\u201334.","journal-title":"Cell Metab"},{"issue":"8","key":"6260_CR66","doi-asserted-by":"publisher","first-page":"519","DOI":"10.4155\/bio-2017-0281","volume":"10","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Liu X. Machine learning techniques for mass spectrometry imaging data analysis and applications. Bioanalysis. 2018;10(8):519\u201322.","journal-title":"Bioanalysis"},{"key":"6260_CR67","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-1-0716-3319-9_17","volume":"2688","author":"MR Clench","year":"2023","unstructured":"Clench MR, Cole LM. Perspective: mass spectrometry imaging - the next 5 years. Methods Mol Biol. 2023;2688:203\u201310.","journal-title":"Methods Mol Biol"},{"issue":"2","key":"6260_CR68","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1021\/acs.jproteome.2c00632","volume":"22","author":"W Bittremieux","year":"2023","unstructured":"Bittremieux W, Levitsky L, Pilz M, Sachsenberg T, Huber F, Wang M, et al. Unified and standardized mass spectrometry data processing in Python using spectrum_utils. J Proteome Res. 2023;22(2):625\u201331.","journal-title":"J Proteome Res"},{"key":"6260_CR69","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac331","author":"X Shen","year":"2022","unstructured":"Shen X, Shao W, Wang C, Liang L, Chen S, Zhang S, et al. Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine. Brief Bioinform. 2022. https:\/\/doi.org\/10.1093\/bib\/bbac331.","journal-title":"Brief Bioinform"},{"issue":"10","key":"6260_CR70","doi-asserted-by":"publisher","first-page":"102541","DOI":"10.1016\/j.jiph.2024.102541","volume":"17","author":"TH Lin","year":"2024","unstructured":"Lin TH, Chung HY, Jian MJ, Chang CK, Lin HH, Yu CM, et al. Artificial intelligence-clinical decision support system for enhanced infectious disease management: accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae. J Infect Public Health. 2024;17(10):102541.","journal-title":"J Infect Public Health"},{"issue":"7","key":"6260_CR71","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1080\/17460441.2024.2354871","volume":"19","author":"C Liu","year":"2024","unstructured":"Liu C, Zhang H. Data processing for high-throughput mass spectrometry in drug discovery. Expert Opin Drug Discov. 2024;19(7):815\u201325.","journal-title":"Expert Opin Drug Discov"},{"key":"6260_CR72","doi-asserted-by":"publisher","first-page":"133870","DOI":"10.1016\/j.foodchem.2022.133870","volume":"398","author":"AN de Oliveira","year":"2023","unstructured":"de Oliveira AN, Bolognini SRF, Navarro LC, Delafiori J, Sales GM, de Oliveira DN, et al. Tomato classification using mass spectrometry-machine learning technique: a food safety-enhancing platform. Food Chem. 2023;398:133870.","journal-title":"Food Chem"},{"issue":"4","key":"6260_CR73","doi-asserted-by":"publisher","first-page":"710","DOI":"10.55730\/1300-0144.5840","volume":"54","author":"P Soylu \u00dcstkoyuncu","year":"2024","unstructured":"Soylu \u00dcstkoyuncu P, \u00dcstkoyuncu N. Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks. Turk J Med Sci. 2024;54(4):710\u20137.","journal-title":"Turk J Med Sci"},{"issue":"7","key":"6260_CR74","doi-asserted-by":"publisher","first-page":"109541","DOI":"10.1016\/j.ejso.2024.109541","volume":"51","author":"C Sagiv","year":"2025","unstructured":"Sagiv C, Hadar O, Najjar A, Pahnke J. Artificial intelligence in surgical pathology - where do we stand, where do we go? Eur J Surg Oncol. 2025;51(7):109541.","journal-title":"Eur J Surg Oncol"},{"key":"6260_CR75","doi-asserted-by":"publisher","first-page":"e58039","DOI":"10.2196\/58039","volume":"26","author":"MJ Jian","year":"2024","unstructured":"Jian MJ, Lin TH, Chung HY, Chang CK, Perng CL, Chang FY, et al. Pioneering Klebsiella pneumoniae antibiotic resistance prediction with artificial intelligence-clinical decision support system-enhanced matrix-assisted laser desorption\/ionization time-of-flight mass spectrometry: retrospective study. J Med Internet Res. 2024;26:e58039.","journal-title":"J Med Internet Res"},{"issue":"2","key":"6260_CR76","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1021\/acs.jproteome.2c00714","volume":"22","author":"YR Xie","year":"2023","unstructured":"Xie YR, Chari VK, Castro DC, Grant R, Rubakhin SS, Sweedler JV. Data-driven and machine learning-based framework for image-guided single-cell mass spectrometry. J Proteome Res. 2023;22(2):491\u2013500.","journal-title":"J Proteome Res"},{"key":"6260_CR77","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-1-0716-2030-4_18","volume":"2437","author":"X Tian","year":"2022","unstructured":"Tian X, Zou Z, Yang Z. Extract metabolomic information from mass spectrometry images using advanced data analysis. Methods Mol Biol. 2022;2437:253\u201372.","journal-title":"Methods Mol Biol"},{"issue":"3","key":"6260_CR78","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1021\/acs.jproteome.2c00711","volume":"22","author":"BA Neely","year":"2023","unstructured":"Neely BA, Dorfer V, Martens L, Bludau I, Bouwmeester R, Degroeve S, et al. Toward an integrated machine learning model of a proteomics experiment. J Proteome Res. 2023;22(3):681\u201396.","journal-title":"J Proteome Res"},{"key":"6260_CR79","doi-asserted-by":"publisher","first-page":"113844","DOI":"10.1016\/j.addr.2021.113844","volume":"176","author":"Q Xiao","year":"2021","unstructured":"Xiao Q, Zhang F, Xu L, Yue L, Kon OL, Zhu Y, et al. High-throughput proteomics and AI for cancer biomarker discovery. Adv Drug Deliv Rev. 2021;176:113844.","journal-title":"Adv Drug Deliv Rev"},{"key":"6260_CR80","doi-asserted-by":"publisher","first-page":"461713","DOI":"10.1016\/j.chroma.2020.461713","volume":"1635","author":"X Fan","year":"2021","unstructured":"Fan X, Ma P, Hou M, Ni Y, Fang Z, Lu H, et al. Deep-learning-assisted multivariate curve resolution. J Chromatogr A. 2021;1635:461713.","journal-title":"J Chromatogr A"},{"issue":"9","key":"6260_CR81","doi-asserted-by":"publisher","first-page":"e2200179","DOI":"10.1002\/pmic.202200179","volume":"23","author":"B Sun","year":"2023","unstructured":"Sun B, Smialowski P, Aftab W, Schmidt A, Forne I, Straub T, et al. Improving SWATH-MS analysis by deep-learning. Proteomics. 2023;23(9):e2200179.","journal-title":"Proteomics"},{"issue":"6","key":"6260_CR82","doi-asserted-by":"publisher","first-page":"e4333","DOI":"10.1002\/pro.4333","volume":"31","author":"TM Allison","year":"2022","unstructured":"Allison TM, Degiacomi MT, Marklund EG, Jovine L, Elofsson A, Benesch JLP, et al. Complementing machine learning-based structure predictions with native mass spectrometry. Protein Sci. 2022;31(6):e4333.","journal-title":"Protein Sci"},{"key":"6260_CR83","doi-asserted-by":"publisher","first-page":"102880","DOI":"10.1016\/j.sbi.2024.102880","volume":"88","author":"RK Goel","year":"2024","unstructured":"Goel RK, Bithi N, Emili A. Trends in co-fractionation mass spectrometry: a new gold-standard in global protein interaction network discovery. Curr Opin Struct Biol. 2024;88:102880.","journal-title":"Curr Opin Struct Biol"},{"key":"6260_CR84","doi-asserted-by":"publisher","first-page":"464041","DOI":"10.1016\/j.chroma.2023.464041","volume":"1700","author":"S Squara","year":"2023","unstructured":"Squara S, Caratti A, Fina A, Liberto E, Spigolon N, Genova G, et al. Artificial intelligence decision-making tools based on comprehensive two-dimensional gas chromatography data: the challenge of quantitative volatilomics in food quality assessment. J Chromatogr A. 2023;1700:464041.","journal-title":"J Chromatogr A"},{"issue":"1","key":"6260_CR85","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s41597-024-02922-z","volume":"11","author":"H Webel","year":"2024","unstructured":"Webel H, Perez-Riverol Y, Nielsen AB, Rasmussen S. Mass spectrometry-based proteomics data from thousands of HeLa control samples. Sci Data. 2024;11(1):112.","journal-title":"Sci Data"},{"key":"6260_CR86","doi-asserted-by":"publisher","DOI":"10.3390\/molecules28104050","author":"X Li","year":"2023","unstructured":"Li X, Wang H, Jiang M, Ding M, Xu X, Xu B, et al. Collision cross section prediction based on machine learning. Molecules. 2023. https:\/\/doi.org\/10.3390\/molecules28104050.","journal-title":"Molecules"},{"issue":"1","key":"6260_CR87","doi-asserted-by":"publisher","first-page":"7975","DOI":"10.1038\/s41598-019-44215-1","volume":"9","author":"M Deulofeu","year":"2019","unstructured":"Deulofeu M, Kol\u00e1\u0159ov\u00e1 L, Salvad\u00f3 V, Mar\u00eda Pe\u00f1a-M\u00e9ndez E, Alm\u00e1\u0161i M, \u0160tork M, et al. Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma. Sci Rep. 2019;9(1):7975.","journal-title":"Sci Rep"},{"issue":"2","key":"6260_CR88","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1021\/jasms.2c00268","volume":"34","author":"F Golpelichi","year":"2023","unstructured":"Golpelichi F, Parastar H. Quantitative mass spectrometry imaging using multivariate curve resolution and deep learning: a case study. J Am Soc Mass Spectrom. 2023;34(2):236\u201344.","journal-title":"J Am Soc Mass Spectrom"},{"issue":"23","key":"6260_CR89","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.1021\/acs.jcim.2c01161","volume":"62","author":"M Mou","year":"2022","unstructured":"Mou M, Pan Z, Lu M, Sun H, Wang Y, Luo Y, et al. Application of machine learning in spatial proteomics. J Chem Inf Model. 2022;62(23):5875\u201395.","journal-title":"J Chem Inf Model"},{"issue":"4","key":"6260_CR90","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1002\/mas.21535","volume":"37","author":"T Kind","year":"2018","unstructured":"Kind T, Tsugawa H, Cajka T, Ma Y, Lai Z, Mehta SS, et al. Identification of small molecules using accurate mass MS\/MS search. Mass Spectrom Rev. 2018;37(4):513\u201332.","journal-title":"Mass Spectrom Rev"},{"issue":"12","key":"6260_CR91","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1515\/cclm-2022-0470","volume":"60","author":"G Constantinescu","year":"2022","unstructured":"Constantinescu G, Schulze M, Peitzsch M, Hofmockel T, Scholl UI, Williams TA, et al. Integration of artificial intelligence and plasma steroidomics with laboratory information management systems: application to primary aldosteronism. Clin Chem Lab Med. 2022;60(12):1929\u201337.","journal-title":"Clin Chem Lab Med"},{"issue":"46","key":"6260_CR92","doi-asserted-by":"publisher","first-page":"17082","DOI":"10.1021\/acs.analchem.3c04016","volume":"95","author":"LE Gonzalez","year":"2023","unstructured":"Gonzalez LE, Snyder DT, Casey H, Hu Y, Wang DM, Guetzloff M, et al. Machine-learning classification of bacteria using two-dimensional tandem mass spectrometry. Anal Chem. 2023;95(46):17082\u20138.","journal-title":"Anal Chem"},{"key":"6260_CR93","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/978-1-4939-2239-0_6","volume":"1260","author":"JH Miller","year":"2015","unstructured":"Miller JH, Schrom BT, Kangas LJ. Artificial neural network for charge prediction in metabolite identification by mass spectrometry. Methods Mol Biol. 2015;1260:89\u2013100.","journal-title":"Methods Mol Biol"},{"issue":"7","key":"6260_CR94","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1021\/acs.jproteome.2c00211","volume":"21","author":"AB Dincer","year":"2022","unstructured":"Dincer AB, Lu Y, Schweppe DK, Oh S, Noble WS. Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning. J Proteome Res. 2022;21(7):1771\u201382.","journal-title":"J Proteome Res"},{"issue":"36","key":"6260_CR95","doi-asserted-by":"publisher","first-page":"13431","DOI":"10.1021\/acs.analchem.3c00613","volume":"95","author":"K Seddiki","year":"2023","unstructured":"Seddiki K, Precioso FE, Sanabria M, Salzet M, Fournier I, Droit A. Early diagnosis: end-to-end CNN-LSTM models for mass spectrometry data classification. Anal Chem. 2023;95(36):13431\u20137.","journal-title":"Anal Chem"},{"issue":"1","key":"6260_CR96","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.tibtech.2019.07.009","volume":"38","author":"CJ Vavricka","year":"2020","unstructured":"Vavricka CJ, Hasunuma T, Kondo A. Dynamic metabolomics for engineering biology: accelerating learning cycles for bioproduction. Trends Biotechnol. 2020;38(1):68\u201382.","journal-title":"Trends Biotechnol"},{"issue":"2","key":"6260_CR97","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.tibs.2024.11.005","volume":"50","author":"C Van Leene","year":"2025","unstructured":"Van Leene C, Van Moortel L, De Bosscher K, Gevaert K. Exploring protein conformations with limited proteolysis coupled to mass spectrometry. Trends Biochem Sci. 2025;50(2):143\u201355.","journal-title":"Trends Biochem Sci"},{"issue":"43","key":"6260_CR98","doi-asserted-by":"publisher","first-page":"14383","DOI":"10.1021\/acs.analchem.1c01692","volume":"93","author":"N Ogrinc","year":"2021","unstructured":"Ogrinc N, Kruszewski A, Chaillou P, Saudemont P, Lagadec C, Salzet M, et al. Robot-assisted SpiderMass for in vivo real-time topography mass spectrometry imaging. Anal Chem. 2021;93(43):14383\u201391.","journal-title":"Anal Chem"},{"issue":"32","key":"6260_CR99","doi-asserted-by":"publisher","first-page":"14590","DOI":"10.1021\/jacs.2c03631","volume":"144","author":"DA Boiko","year":"2022","unstructured":"Boiko DA, Kozlov KS, Burykina JV, Ilyushenkova VV, Ananikov VP. Fully automated unconstrained analysis of high-resolution mass spectrometry data with machine learning. J Am Chem Soc. 2022;144(32):14590\u2013606.","journal-title":"J Am Chem Soc"},{"issue":"10","key":"6260_CR100","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1039\/D1NP00014D","volume":"38","author":"H Tsugawa","year":"2021","unstructured":"Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep. 2021;38(10):1729\u201359.","journal-title":"Nat Prod Rep"},{"issue":"9","key":"6260_CR101","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1021\/jasms.2c00080","volume":"33","author":"H Yamada","year":"2022","unstructured":"Yamada H, Xu L, Eto F, Takeichi R, Islam A, Mamun MA, et al. Changes of mass spectra patterns on a brain tissue section revealed by deep learning with imaging mass spectrometry data. J Am Soc Mass Spectrom. 2022;33(9):1607\u201314.","journal-title":"J Am Soc Mass Spectrom"},{"issue":"5","key":"6260_CR102","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s11948-023-00451-3","volume":"29","author":"R Alvarado","year":"2023","unstructured":"Alvarado R. AI as an epistemic technology. Sci Eng Ethics. 2023;29(5):32.","journal-title":"Sci Eng Ethics"}],"container-title":["Analytical and Bioanalytical Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00216-025-06260-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00216-025-06260-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00216-025-06260-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:02:18Z","timestamp":1773813738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00216-025-06260-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":102,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["6260"],"URL":"https:\/\/doi.org\/10.1007\/s00216-025-06260-w","relation":{},"ISSN":["1618-2642","1618-2650"],"issn-type":[{"value":"1618-2642","type":"print"},{"value":"1618-2650","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"23 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}