{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T03:55:48Z","timestamp":1785902148986,"version":"3.56.0"},"reference-count":74,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,2,1]],"date-time":"2027-02-01T00:00:00Z","timestamp":1801440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22374124"],"award-info":[{"award-number":["22374124"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22174118"],"award-info":[{"award-number":["22174118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Letters"],"published-print":{"date-parts":[[2027,2]]},"DOI":"10.1016\/j.mrl.2026.200290","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:16:24Z","timestamp":1780013784000},"page":"200290","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["The perspective of artificial intelligence for NMR"],"prefix":"10.1016","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0596-0740","authenticated-orcid":false,"given":"Yanqin","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maili","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.mrl.2026.200290_bib1","article-title":"Deep learning and its applications in nuclear magnetic resonance spectroscopy","volume":"146\u2013147","author":"Luo","year":"2025","journal-title":"Prog. Nucl. Magn. Reson. Spectrosc."},{"key":"10.1016\/j.mrl.2026.200290_bib2","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"10.1016\/j.mrl.2026.200290_bib3","doi-asserted-by":"crossref","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":"10.1016\/j.mrl.2026.200290_bib4","doi-asserted-by":"crossref","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":"10.1016\/j.mrl.2026.200290_bib5","first-page":"179","article-title":"Finding structure in time, Cognitive","volume":"14","author":"Elman","year":"1990","journal-title":"Science"},{"key":"10.1016\/j.mrl.2026.200290_bib6","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.mrl.2026.200290_bib7","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.mrl.2026.200290_bib8","article-title":"GPT-4 technical report","author":"Achiam","year":"2024","journal-title":"arXiv preprint arXiv:2303.08774"},{"key":"10.1016\/j.mrl.2026.200290_bib9","doi-asserted-by":"crossref","DOI":"10.1126\/science.adm7168","article-title":"Whole-body magnetic resonance imaging at 0.05 Tesla","volume":"384","author":"Zhao","year":"2024","journal-title":"Science"},{"key":"10.1016\/j.mrl.2026.200290_bib10","article-title":"Fragment-based discovery of TopBP1 inhibitors integrated with AI-driven molecular docking","author":"Chen","year":"2025","journal-title":"Magn. Reson. Lett."},{"key":"10.1016\/j.mrl.2026.200290_bib11","article-title":"Cloud-magnetic resonance imaging system: in the era of 6G and artificial intelligence","volume":"5","author":"Zhou","year":"2025","journal-title":"Magn. Reson. Lett."},{"key":"10.1016\/j.mrl.2026.200290_bib12","article-title":"Artificial intelligence goes from predicting structure to predicting stability","volume":"5","author":"Pielak","year":"2025","journal-title":"Magn. Reson. Lett."},{"key":"10.1016\/j.mrl.2026.200290_bib13","doi-asserted-by":"crossref","first-page":"10383","DOI":"10.1002\/ange.201908162","article-title":"Accelerated nuclear magnetic resonance spectroscopy with deep learning","volume":"132","author":"Qu","year":"2020","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.mrl.2026.200290_bib14","article-title":"Deep learning network for NMR spectra reconstruction in time-frequency domain and quality assessment","volume":"16","author":"Luo","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.mrl.2026.200290_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmr.2020.106772","article-title":"Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network","volume":"317","author":"Luo","year":"2020","journal-title":"J. Magn. Reson."},{"key":"10.1016\/j.mrl.2026.200290_bib16","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10858-021-00366-w","article-title":"FID-Net: a versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling","volume":"75","author":"Karunanithy","year":"2021","journal-title":"J. Biomol. NMR"},{"key":"10.1016\/j.mrl.2026.200290_bib17","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1021\/acs.analchem.0c03087","article-title":"Improvement in signal-to-noise ratio of liquid-state NMR spectroscopy via a deep neural network DN-Unet","volume":"93","author":"Wu","year":"2021","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmr.2022.107357","article-title":"Deconvolution of 1D NMR spectra: a deep learning-based approach","volume":"347","author":"Schmid","year":"2023","journal-title":"J. Magn. Reson."},{"key":"10.1016\/j.mrl.2026.200290_bib19","doi-asserted-by":"crossref","first-page":"19","DOI":"10.5194\/mr-4-19-2023","article-title":"Deep picker1D and voigt fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra","volume":"4","author":"Li","year":"2023","journal-title":"Magnes. Res."},{"key":"10.1016\/j.mrl.2026.200290_bib20","doi-asserted-by":"crossref","first-page":"2590","DOI":"10.1093\/bioinformatics\/bty134","article-title":"NMRNet: a deep learning approach to automated peak picking of protein NMR spectra","volume":"34","author":"Klukowski","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.mrl.2026.200290_bib21","doi-asserted-by":"crossref","first-page":"16935","DOI":"10.1021\/jacs.1c04010","article-title":"Virtual homonuclear decoupling in direct detection nuclear magnetic resonance experiments using deep neural networks","volume":"143","author":"Karunanithy","year":"2021","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.mrl.2026.200290_bib22","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-024-49378-8","article-title":"Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks","volume":"15","author":"Karunanithy","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.mrl.2026.200290_bib23","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.adr2155","article-title":"A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins","volume":"10","author":"Shukla","year":"2024","journal-title":"Sci. Adv."},{"key":"10.1016\/j.mrl.2026.200290_bib24","doi-asserted-by":"crossref","first-page":"20039","DOI":"10.1039\/D4SC04742G","article-title":"Deep learning enabled ultra-high quality NMR chemical shift resolved spectra","volume":"15","author":"Yang","year":"2024","journal-title":"Chem. Sci."},{"key":"10.1016\/j.mrl.2026.200290_bib25","doi-asserted-by":"crossref","first-page":"15686","DOI":"10.1021\/acs.analchem.9b03849","article-title":"Accurate and efficient determination of unknown metabolites in metabolomics by NMR-based molecular motif identification","volume":"91","author":"Wang","year":"2019","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib26","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1093\/bib\/bbaa204","article-title":"Deep learning meets metabolomics: a methodological perspective","volume":"22","author":"Sen","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.mrl.2026.200290_bib27","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv preprint arXiv:1806.01261"},{"key":"10.1016\/j.mrl.2026.200290_bib28","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.mrl.2026.200290_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jmr.2024.107792","article-title":"Neural net analysis of NMR spectra from strongly-coupled spin systems","volume":"368","author":"Prestegard","year":"2024","journal-title":"J. Magn. Reson."},{"key":"10.1016\/j.mrl.2026.200290_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodchem.2025.145181","article-title":"Low-field NMR-based deep learning for non-destructive quality assessment of frozen model foods","volume":"489","author":"Jiang","year":"2025","journal-title":"Food Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib31","doi-asserted-by":"crossref","first-page":"7143","DOI":"10.1002\/anie.201300129","article-title":"Simplifying proton NMR spectra by instant homonuclear broadband decoupling","volume":"52","author":"Meyer","year":"2013","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.mrl.2026.200290_bib32","doi-asserted-by":"crossref","first-page":"6990","DOI":"10.1002\/anie.201404111","article-title":"Ultrahigh-resolution NMR spectroscopy","volume":"53","author":"Foroozandeh","year":"2014","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.mrl.2026.200290_bib33","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1021\/acs.jpclett.2c00100","article-title":"Fast acquisition of high-quality nuclear magnetic resonance pure shift spectroscopy via a deep neural network","volume":"13","author":"Zheng","year":"2022","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.mrl.2026.200290_bib34","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10858-017-0105-x","article-title":"Pitfalls in compressed sensing reconstruction and how to avoid them","volume":"68","author":"Shchukina","year":"2017","journal-title":"J. Biomol. NMR"},{"key":"10.1016\/j.mrl.2026.200290_bib35","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1021\/acs.analchem.3c04007","article-title":"Accelerated pure shift NMR spectroscopy with deep learning","volume":"96","author":"Zhan","year":"2024","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib36","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202309810","article-title":"Fast pure shift NMR spectroscopy using attention-assisted deep neural network","volume":"11","author":"Zhan","year":"2024","journal-title":"Adv. Sci."},{"key":"10.1016\/j.mrl.2026.200290_bib37","doi-asserted-by":"crossref","DOI":"10.1038\/s42004-026-01912-z","article-title":"Physics-informed deep learning enables fast ultrahigh-resolution nuclear magnetic resonance spectroscopy","author":"Bao","year":"2026","journal-title":"Commun. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib38","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2025.3552862","article-title":"Hybrid domain enhancement network for ultraclean pure shift spectroscopy with fast acquisition","volume":"74","author":"Shen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.mrl.2026.200290_bib39","doi-asserted-by":"crossref","first-page":"13480","DOI":"10.1021\/acs.analchem.5c01899","article-title":"Sensitivity-enhanced pure shift spectroscopy empowered by deep learning and PSYCHE","volume":"97","author":"Zheng","year":"2025","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib40","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1021\/acs.jpclett.3c00455","article-title":"Deep learning methodology for obtaining ultraclean pure shift proton nuclear magnetic resonance spectra","volume":"14","author":"Yang","year":"2023","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.mrl.2026.200290_bib41","article-title":"High-quality NMR pure shift spectra empowered by self-attention deep learning","author":"Cai","year":"2025","journal-title":"ChemRxiv"},{"key":"10.1016\/j.mrl.2026.200290_bib42","article-title":"NMRVPF: a high-precision method for automatic identification and quantitative analysis of mixtures based on 1D NMR spectra","author":"Liu","year":"2026","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression - a tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"10.1016\/j.mrl.2026.200290_bib44","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1021\/acs.analchem.7b03795","article-title":"Application of a deep neural network to metabolomics studies and its performance in determining important variables","volume":"90","author":"Date","year":"2018","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib45","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.mrl.2026.200290_bib46","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.mrl.2026.200290_bib47","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.aca.2018.02.045","article-title":"Application of ensemble deep neural network to metabolomics studies","volume":"1037","author":"Asakura","year":"2018","journal-title":"Anal. Chim. Acta"},{"key":"10.1016\/j.mrl.2026.200290_bib48","doi-asserted-by":"crossref","DOI":"10.3390\/metabo11120863","article-title":"Deep learning for rapid identification of microbes using metabolomics profiles","volume":"11","author":"Wang","year":"2021","journal-title":"Metabolites"},{"key":"10.1016\/j.mrl.2026.200290_bib49","doi-asserted-by":"crossref","first-page":"4114","DOI":"10.1021\/jacs.9b13786","article-title":"A convolutional neural network-based approach for the rapid annotation of molecularly diverse natural products","volume":"142","author":"Reher","year":"2020","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.mrl.2026.200290_bib50","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1002\/mrc.5240","article-title":"SMART-Miner: a convolutional neural network-based metabolite identification from 1H-13C HSQC spectra","volume":"60","author":"Kim","year":"2022","journal-title":"Magn. Reson. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib51","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1080\/14686996.2020.1793382","article-title":"NMR-TS: de novo molecule identification from NMR spectra","volume":"21","author":"Zhang","year":"2020","journal-title":"Sci. Technol. Adv. Mater."},{"key":"10.1016\/j.mrl.2026.200290_bib52","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-47434-8","article-title":"1H NMR-MS-based heterocovariance as a drug discovery tool for fishing bioactive compounds out of a complex mixture of structural analogues","volume":"9","author":"Grienke","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.mrl.2026.200290_bib53","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.aca.2014.07.009","article-title":"Dereplication of depsides from the lichen pseudevernia furfuracea by centrifugal partition chromatography combined to 13C nuclear magnetic resonance pattern recognition","volume":"846","author":"Oettl","year":"2014","journal-title":"Anal. Chim. Acta"},{"key":"10.1016\/j.mrl.2026.200290_bib54","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1021\/acs.jnatprod.6b01063","article-title":"Computer-aided 13C NMR chemical profiling of crude natural extracts without fractionation","volume":"80","author":"Bakiri","year":"2017","journal-title":"J. Nat. Prod."},{"key":"10.1016\/j.mrl.2026.200290_bib55","doi-asserted-by":"crossref","first-page":"8793","DOI":"10.1021\/acs.analchem.0c00193","article-title":"MixONat, a software for the dereplication of mixtures based on 13C NMR spectroscopy","volume":"92","author":"Bruguiere","year":"2020","journal-title":"Anal. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib56","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1055\/a-1470-0446","article-title":"13C NMR dereplication using MixONat software: a practical guide to decipher natural products mixtures","volume":"87","author":"Bruguiere","year":"2021","journal-title":"Planta Med."},{"key":"10.1016\/j.mrl.2026.200290_bib57","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1021\/acs.jnatprod.0c01076","article-title":"Development of an NMR-based platform for the direct structural annotation of complex natural products mixtures","volume":"84","author":"Egan","year":"2021","journal-title":"J. Nat. Prod."},{"key":"10.1016\/j.mrl.2026.200290_bib58","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1021\/acs.jnatprod.1c00841","article-title":"Dereplication of fungal metabolites by NMR-based compound networking using MADByTE","volume":"85","author":"Flores-Bocanegra","year":"2022","journal-title":"J. Nat. Prod."},{"key":"10.1016\/j.mrl.2026.200290_bib59","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1161\/CIRCULATIONAHA.108.816181","article-title":"Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women","volume":"119","author":"Mora","year":"2009","journal-title":"Circulation"},{"key":"10.1016\/j.mrl.2026.200290_bib60","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1016\/j.numecd.2023.06.012","article-title":"Lipoprotein profile assessed by 1H NMR, BMI and blood pressure are associated with vascular alterations in children with familial hypercholesterolaemia","volume":"33","author":"Llop","year":"2023","journal-title":"Nutr. Metabol. Cardiovasc. Dis."},{"key":"10.1016\/j.mrl.2026.200290_bib61","doi-asserted-by":"crossref","first-page":"e2090","DOI":"10.1210\/clinem\/dgae037","article-title":"The lipoprotein profile evaluated by 1H-NMR improves the performance of genetic testing in familial hypercholesterolemia","volume":"109","author":"Daiana","year":"2024","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"10.1016\/j.mrl.2026.200290_bib62","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1021\/acs.jproteome.6b00975","article-title":"NMR-based lipid profiling of high density lipoprotein particles in healthy subjects with low, normal, and elevated HDL-cholesterol","volume":"16","author":"Kostara","year":"2017","journal-title":"J. Proteome Res."},{"key":"10.1016\/j.mrl.2026.200290_bib63","doi-asserted-by":"crossref","DOI":"10.1007\/s11306-025-02262-y","article-title":"Longitudinal lipoprotein and inflammatory mediators analysis uncover persisting inflammation and hyperlipidemia following SARS-CoV-2 infection in long COVID-19","volume":"21","author":"Bae","year":"2025","journal-title":"Metabolomics"},{"key":"10.1016\/j.mrl.2026.200290_bib64","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1002\/nbm.1940080603","article-title":"Quantification of biomedical NMR data using artificial neural network analysis: lipoprotein lipid profiles from H-1 NMR data of human plasma","volume":"8","author":"AlaKorpela","year":"1995","journal-title":"NMR Biomed."},{"key":"10.1016\/j.mrl.2026.200290_bib65","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1006\/jmrb.1995.1032","article-title":"Lipoprotein lipid quantification by neural-network analysis of H-1-NMR data from human blood-plasma","volume":"106","author":"Hiltunen","year":"1995","journal-title":"J. Magn. Reson., Ser. B"},{"key":"10.1016\/j.mrl.2026.200290_bib66","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1093\/clinchem\/18.6.499","article-title":"Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge","volume":"18","author":"Friedewald","year":"1972","journal-title":"Clin. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib67","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0025-7125(16)30178-X","article-title":"Epidemiology of triglycerides, small dense low-density-lipoprotein, and lipoprotein(a) as risk-factors for coronary heart-disease","volume":"78","author":"Austin","year":"1994","journal-title":"Med. Clin. North Am."},{"key":"10.1016\/j.mrl.2026.200290_bib68","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.1038\/nprot.2007.376","article-title":"Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts","volume":"2","author":"Beckonert","year":"2007","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.mrl.2026.200290_bib69","doi-asserted-by":"crossref","DOI":"10.1038\/s42004-024-01251-x","article-title":"Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment","volume":"7","author":"Xiao","year":"2024","journal-title":"Commun. Chem."},{"key":"10.1016\/j.mrl.2026.200290_bib70","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1039\/C3AY41907J","article-title":"Principal component analysis","volume":"6","author":"Bro","year":"2014","journal-title":"Anal. Methods"},{"key":"10.1016\/j.mrl.2026.200290_bib71","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-025-65294-x","article-title":"Deriving three one dimensional NMR spectra from a single experiment through machine learning","volume":"16","author":"Vignoli","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.mrl.2026.200290_bib72","doi-asserted-by":"crossref","DOI":"10.1016\/j.ab.2024.115654","article-title":"The development of machine learning approaches in two-dimensional NMR data interpretation for metabolomics applications","volume":"695","author":"Pollak","year":"2024","journal-title":"Anal. Biochem."},{"key":"10.1016\/j.mrl.2026.200290_bib73","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1038\/s41591-022-01980-3","article-title":"Metabolomic profiles predict individual multidisease outcomes","volume":"28","author":"Buergel","year":"2022","journal-title":"Nat. Med."},{"key":"10.1016\/j.mrl.2026.200290_bib74","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacadv.2025.101620","article-title":"Unsupervised learning-derived complex metabolic signatures refine cardiometabolic risk","volume":"4","author":"Zhou","year":"2025","journal-title":"JACC-Adv."}],"container-title":["Magnetic Resonance Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772516226000343?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2772516226000343?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:16:38Z","timestamp":1784531798000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2772516226000343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,2]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2027,2]]}},"alternative-id":["S2772516226000343"],"URL":"https:\/\/doi.org\/10.1016\/j.mrl.2026.200290","relation":{},"ISSN":["2772-5162"],"issn-type":[{"value":"2772-5162","type":"print"}],"subject":[],"published":{"date-parts":[[2027,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"The perspective of artificial intelligence for NMR","name":"articletitle","label":"Article Title"},{"value":"Magnetic Resonance Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mrl.2026.200290","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}],"article-number":"200290"}}