{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T05:44:28Z","timestamp":1781934268738,"version":"3.54.5"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01DA048993"],"award-info":[{"award-number":["R01DA048993"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH105561"],"award-info":[{"award-number":["R01MH105561"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM124061"],"award-info":[{"award-number":["R01GM124061"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS2123777"],"award-info":[{"award-number":["IIS2123777"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Talent Program","award":["2021CX02Y145"],"award-info":[{"award-number":["2021CX02Y145"]}]},{"name":"Guangdong Provincial Key Laboratory of Big Data Computing and Shenzhen Key Laboratory of Cross-Modal Cognitive Computing","award":["ZDSYS20230626091302006"],"award-info":[{"award-number":["ZDSYS20230626091302006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.<\/jats:p>","DOI":"10.1093\/bib\/bbae141","type":"journal-article","created":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T13:39:25Z","timestamp":1712410765000},"source":"Crossref","is-referenced-by-count":4,"title":["Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes"],"prefix":"10.1093","volume":"25","author":[{"given":"Guoxuan","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute of Big Data, School of Data Science , The Chinese University of Hong Kong - Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong 518172 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"2024040613390563600_ref1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.envint.2018.07.044","article-title":"Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution","volume":"120","author":"Liang","year":"2018","journal-title":"Environ Int"},{"issue":"3","key":"2024040613390563600_ref2","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/mas.21548","article-title":"Metabolomics toward personalized medicine","volume":"38","author":"Jacob","year":"2019","journal-title":"Mass Spectrom Rev"},{"key":"2024040613390563600_ref3","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.copbio.2018.07.010","article-title":"Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics","volume":"55","author":"Chaleckis","year":"2019","journal-title":"Curr Opin Biotechnol"},{"issue":"1","key":"2024040613390563600_ref4","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/ac202450g","article-title":"Camera: an integrated strategy for compound spectra extraction and annotation of liquid chromatography\/mass spectrometry data sets","volume":"84","author":"Kuhl","year":"2012","journal-title":"Anal Chem"},{"key":"2024040613390563600_ref5","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/1471-2105-14-15","article-title":"xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data","volume":"14","author":"Uppal","year":"2013","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2024040613390563600_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-09550-x","article-title":"Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics","volume":"10","author":"Shen","year":"2019","journal-title":"Nat Commun"},{"issue":"2","key":"2024040613390563600_ref7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3390\/metabo8020031","article-title":"Software tools and approaches for compound identification of lc-ms\/ms data in metabolomics","volume":"8","author":"Blazenovic","year":"2018","journal-title":"Metabolites"},{"issue":"7","key":"2024040613390563600_ref8","doi-asserted-by":"crossref","first-page":"e1003123","DOI":"10.1371\/journal.pcbi.1003123","article-title":"Predicting network activity from high throughput metabolomics","volume":"9","author":"Li","year":"2013","journal-title":"PLoS Comput Biol"},{"issue":"W1","key":"2024040613390563600_ref9","doi-asserted-by":"crossref","first-page":"W486","DOI":"10.1093\/nar\/gky310","article-title":"Metaboanalyst 4.0: towards more transparent and integrative metabolomics analysis","volume":"46","author":"Chong","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2024040613390563600_ref10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-017-2006-0","article-title":"Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data","volume":"19","author":"Marco-Ramell","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2024040613390563600_ref11","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/978-1-0716-0239-3_19","article-title":"Pathway analysis for targeted and untargeted metabolomics","volume":"2104","author":"Karnovsky","year":"2020","journal-title":"Methods Mol Biol"},{"issue":"13","key":"2024040613390563600_ref12","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1002\/sim.8957","article-title":"Pathway testing for longitudinal metabolomics","volume":"40","author":"Ebrahimpoor","year":"2021","journal-title":"Stat Med"},{"issue":"3","key":"2024040613390563600_ref13","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1021\/acs.jproteome.6b00861","article-title":"Network marker selection for untargeted LC-MS metabolomics data","volume":"16","author":"Cai","year":"2017","journal-title":"J Proteome Res"},{"issue":"4","key":"2024040613390563600_ref14","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s11306-018-1335-y","article-title":"From correlation to causation: analysis of metabolomics data using systems biology approaches","volume":"14","author":"Rosato","year":"2018","journal-title":"Metabolomics"},{"issue":"12","key":"2024040613390563600_ref15","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1093\/bioinformatics\/btm129","article-title":"A Markov random field model for network-based analysis of genomic data","volume":"23","author":"Wei","year":"2007","journal-title":"Bioinformatics"},{"issue":"2","key":"2024040613390563600_ref16","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1111\/j.1541-0420.2009.01296.x","article-title":"Incorporating predictor network in penalized regression with application to microarray data","volume":"66","author":"Pan","year":"2010","journal-title":"Biometrics"},{"issue":"2","key":"2024040613390563600_ref17","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1214\/11-AOAS528","article-title":"More power via graph-structured tests for differential expression of gene networks","volume":"6","author":"Jacob","year":"2012","journal-title":"Ann Appl Stat"},{"issue":"3","key":"2024040613390563600_ref18","first-page":"1433","article-title":"Network-regularized high-dimensional Cox regression for analysis of genomic data","volume":"24","author":"Sun","year":"2014","journal-title":"Stat Sin"},{"issue":"10","key":"2024040613390563600_ref19","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1093\/bioinformatics\/btw833","article-title":"Powerful differential expression analysis incorporating network topology for next-generation sequencing data","volume":"33","author":"Dona","year":"2017","journal-title":"Bioinformatics"},{"issue":"3","key":"2024040613390563600_ref20","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1002\/gepi.22194","article-title":"Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis","volume":"43","author":"Ren","year":"2019","journal-title":"Genet Epidemiol"},{"issue":"2","key":"2024040613390563600_ref21","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1214\/14-AOAS719","article-title":"A Bayesian nonparametric mixture model for selecting genes and gene subnetworks","volume":"8","author":"Zhao","year":"2014","journal-title":"Ann Appl Stat"},{"issue":"7","key":"2024040613390563600_ref22","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1002\/sim.9267","article-title":"Feature selection and classification over the network with missing node observations","volume":"41","author":"Jin","year":"2022","journal-title":"Stat Med"},{"issue":"23","key":"2024040613390563600_ref23","doi-asserted-by":"crossref","first-page":"3685","DOI":"10.1093\/bioinformatics\/btw522","article-title":"Bayesian network feature finder (BANFF): an R package for gene network feature selection","volume":"32","author":"Lan","year":"2016","journal-title":"Bioinformatics"},{"issue":"14","key":"2024040613390563600_ref24","doi-asserted-by":"crossref","first-page":"3662","DOI":"10.1093\/bioinformatics\/btac364","article-title":"Metapone: a bioconductor package for joint pathway testing for untargeted metabolomics data","volume":"38","author":"Tian","year":"2022","journal-title":"Bioinformatics"},{"key":"2024040613390563600_ref25","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1214\/aos\/1176342871","article-title":"Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems","volume":"2","author":"Antoniak","year":"1974","journal-title":"Ann Stat"},{"issue":"425","key":"2024040613390563600_ref26","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1080\/01621459.1994.10476468","article-title":"Estimating normal means with a Dirichlet process prior","volume":"89","author":"Escobar","year":"1994","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"2024040613390563600_ref27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/10618600.2000.10474879","article-title":"Markov chain sampling methods for Dirichlet process mixture models","volume":"9","author":"Neal","year":"2000","journal-title":"J Comput Graph Stat"},{"key":"2024040613390563600_ref28","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1017\/CBO9780511802478.008","article-title":"Nonparametric Bayes applications to biostatistics","volume":"28","author":"Dunson","year":"2010","journal-title":"Bayesian Nonparametrics"},{"key":"2024040613390563600_ref29","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.csda.2019.01.009","article-title":"Bayesian hidden Markov models for dependent large-scale multiple testing","volume":"136","author":"Wang","year":"2019","journal-title":"Comput Stat Data Anal"},{"issue":"453","key":"2024040613390563600_ref30","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1198\/016214501750332758","article-title":"Gibbs sampling methods for stick-breaking priors","volume":"96","author":"Ishwaran","year":"2001","journal-title":"J Am Stat Assoc"},{"issue":"9","key":"2024040613390563600_ref31","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1093\/bioinformatics\/btn081","article-title":"Network-constrained regularization and variable selection for analysis of genomic data","volume":"24","author":"Li","year":"2008","journal-title":"Bioinformatics"},{"issue":"2","key":"2024040613390563600_ref32","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1103\/PhysRevLett.58.86","article-title":"Nonuniversal critical dynamics in Monte Carlo simulations","volume":"58","author":"Swendsen","year":"1987","journal-title":"Phys Rev Lett"},{"issue":"2","key":"2024040613390563600_ref33","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1021\/acs.analchem.6b01214","article-title":"xMSannotator: an R package for network-based annotation of high-resolution metabolomics data","volume":"89","author":"Uppal","year":"2017","journal-title":"Anal Chem"},{"issue":"2","key":"2024040613390563600_ref34","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1111\/j.1541-0420.2007.00895.x","article-title":"Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models","volume":"64","author":"Morris","year":"2008","journal-title":"Biometrics"},{"issue":"1","key":"2024040613390563600_ref35","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1103\/RevModPhys.74.47","article-title":"Statistical mechanics of complex networks","volume":"74","author":"Albert","year":"2002","journal-title":"Rev Mod Phys"},{"key":"2024040613390563600_ref36","article-title":"Study ST001849, project ID PR001166, 2021","author":"NIH NMDR"},{"issue":"8","key":"2024040613390563600_ref37","doi-asserted-by":"crossref","first-page":"100369","DOI":"10.1016\/j.xcrm.2021.100369","article-title":"Longitudinal metabolomics of human plasma reveals prognostic markers of Covid-19 disease severity","volume":"2","author":"Sindelar","year":"2021","journal-title":"Cell Rep Med"},{"issue":"1","key":"2024040613390563600_ref38","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto Encyclopedia of Genes and Genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2024040613390563600_ref39","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1214\/14-AOS1255","article-title":"Partial distance correlation with methods for dissimilarities","volume":"42","author":"Sz\u00e9kely","year":"2014","journal-title":"Ann Stat"},{"issue":"9","key":"2024040613390563600_ref40","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1093\/bioinformatics\/bth088","article-title":"Gostat: find statistically overrepresented gene ontologies within a group of genes","volume":"20","author":"Beissbarth","year":"2004","journal-title":"Bioinformatics"},{"issue":"4","key":"2024040613390563600_ref41","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00109-022-02177-4","article-title":"Disturbed lipid and amino acid metabolisms in COVID-19 patients","volume":"100","author":"Masoodi","year":"2022","journal-title":"J Mol Med"},{"issue":"17","key":"2024040613390563600_ref42","doi-asserted-by":"crossref","DOI":"10.3390\/ijms22179548","article-title":"The serum metabolome of moderate and severe COVID-19 patients reflects possible liver alterations involving carbon and nitrogen metabolism","volume":"22","author":"Caterino","year":"2021","journal-title":"Int J Mol Sci"},{"issue":"1","key":"2024040613390563600_ref43","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s12879-022-07979-y","article-title":"Understanding metabolic alterations after SARS-CoV-2 infection: insights from the patients\u2019 oral microenvironmental metabolites","volume":"23","author":"Ma","year":"2023","journal-title":"BMC Infect Dis"},{"issue":"2","key":"2024040613390563600_ref44","doi-asserted-by":"crossref","first-page":"2100284","DOI":"10.1183\/13993003.00284-2021","article-title":"Metabolomic analyses reveal new stage-specific features of Covid-19","volume":"59","author":"Jia","year":"2022","journal-title":"Eur Respir J"},{"issue":"4","key":"2024040613390563600_ref45","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.niox.2006.04.003","article-title":"Arginine metabolic pathways determine its therapeutic benefit in experimental heatstroke: role of Th1\/Th2 cytokine balance","volume":"15","author":"Chatterjee","year":"2006","journal-title":"Nitric oxide"},{"issue":"11","key":"2024040613390563600_ref46","doi-asserted-by":"crossref","first-page":"5460","DOI":"10.3390\/ijms22115460","article-title":"Possible beneficial actions of caffeine in SARS-CoV-2","volume":"22","author":"Romero-Mart\u00ednez","year":"2021","journal-title":"Int J Mol Sci"},{"issue":"11","key":"2024040613390563600_ref47","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1111\/j.1872-034X.2011.00856.x","article-title":"Oral application of 1,7-dimethylxanthine (paraxanthine) attenuates the formation of experimental cholestatic liver fibrosis","volume":"41","author":"Klemmer","year":"2011","journal-title":"Hepatol Res"},{"issue":"5","key":"2024040613390563600_ref48","doi-asserted-by":"crossref","first-page":"104322","DOI":"10.1016\/j.isci.2022.104322","article-title":"Evidence showing lipotoxicity worsens outcomes in Covid-19 patients and insights about the underlying mechanisms","volume":"25","author":"Cartin-Ceba","year":"2022","journal-title":"iScience"},{"issue":"Pt 2","key":"2024040613390563600_ref49","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1107\/S2059798323000049","article-title":"Cryo-EM reveals binding of linoleic acid to SARS-CoV-2 spike glycoprotein, suggesting an antiviral treatment strategy","volume":"79","author":"Toelzer","year":"2023","journal-title":"Acta Crystallogr D Struct Biol"},{"issue":"11","key":"2024040613390563600_ref50","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1007\/s00228-020-02941-w","article-title":"What about COVID-19 and arachidonic acid pathway?","volume":"76","author":"Hoxha","year":"2020","journal-title":"Eur J Clin Pharmacol"},{"issue":"1","key":"2024040613390563600_ref51","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1038\/s41467-021-21907-9","article-title":"Integrated cytokine and metabolite analysis reveals immunometabolic reprogramming in COVID-19 patients with therapeutic implications","volume":"12","author":"Xiao","year":"2021","journal-title":"Nat Commun"},{"key":"2024040613390563600_ref52","article-title":"Study ST001637, project ID PR001047, 2020","author":"NIH NMDR"},{"issue":"1","key":"2024040613390563600_ref53","doi-asserted-by":"crossref","first-page":"6021","DOI":"10.1038\/s41467-021-26310-y","article-title":"A metabolome atlas of the aging mouse brain","volume":"12","author":"Ding","year":"2021","journal-title":"Nat Commun"},{"issue":"7","key":"2024040613390563600_ref54","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1002\/neu.20242","article-title":"Overview of retinoid metabolism and function","volume":"66","author":"Blomhoff","year":"2006","journal-title":"J Neurobiol"},{"issue":"8","key":"2024040613390563600_ref55","doi-asserted-by":"crossref","first-page":"580","DOI":"10.4149\/BLL_2020_096","article-title":"Low dosages of vitamin a may cause a decrease in the total neuron number of fetal hippocampal rat cells","volume":"121","author":"Ay","year":"2020","journal-title":"Bratisl Med J"},{"key":"2024040613390563600_ref56","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.biocel.2016.12.015","article-title":"Fats for thoughts: an update on brain fatty acid metabolism","volume":"84","author":"Romano","year":"2017","journal-title":"Int J Biochem Cell Biol"},{"key":"2024040613390563600_ref57","doi-asserted-by":"crossref","first-page":"602887","DOI":"10.3389\/fcell.2021.602887","article-title":"High-throughput metabolomics for discovering potential biomarkers and identifying metabolic mechanisms in aging and Alzheimer\u2019s disease","volume":"9","author":"Xie","year":"2021","journal-title":"Front Cell Develop Biol"},{"issue":"5","key":"2024040613390563600_ref58","doi-asserted-by":"crossref","first-page":"e03892","DOI":"10.1016\/j.heliyon.2020.e03892","article-title":"Lauric acid promotes neuronal maturation mediated by astrocytes in primary cortical cultures","volume":"6","author":"Nakajima","year":"2020","journal-title":"Heliyon"},{"issue":"4","key":"2024040613390563600_ref59","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1038\/s42255-022-00559-z","article-title":"Metabolic messengers: bile acids","volume":"4","author":"Perino","year":"2022","journal-title":"Nat Metab"},{"issue":"11","key":"2024040613390563600_ref60","doi-asserted-by":"crossref","first-page":"3658","DOI":"10.1096\/fj.201600275R","article-title":"Effects of bile acids on neurological function and disease","volume":"30","author":"McMillin","year":"2016","journal-title":"FASEB J"},{"key":"2024040613390563600_ref61","doi-asserted-by":"crossref","first-page":"108311","DOI":"10.1016\/j.pharmthera.2022.108311","article-title":"Bile acids and neurological disease","volume":"240","author":"Bates","year":"2022","journal-title":"Pharmacol Ther"},{"issue":"1","key":"2024040613390563600_ref62","doi-asserted-by":"crossref","first-page":"12935","DOI":"10.1038\/s41598-021-92151-w","article-title":"Palmitic acid promotes resistin-induced insulin resistance and inflammation in SH-SY5Y human neuroblastoma","volume":"11","author":"Amine","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"2024040613390563600_ref63","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3233\/ADR-220071","article-title":"The pathological effects of circulating hydrophobic bile acids in Alzheimer\u2019s disease","volume":"7","author":"Ehtezazi","year":"2023","journal-title":"J Alzheimers Dis Rep"},{"key":"2024040613390563600_ref64","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.neuropharm.2015.05.031","article-title":"An introduction to the roles of purinergic signalling in neurodegeneration, neuroprotection and neuroregeneration","volume":"104","author":"Burnstock","year":"2016","journal-title":"Neuropharmacology"},{"issue":"11","key":"2024040613390563600_ref65","doi-asserted-by":"crossref","DOI":"10.3390\/ijms19113598","article-title":"Emerging role of purine metabolizing enzymes in brain function and Tumors","volume":"19","author":"Garcia-Gil","year":"2018","journal-title":"Int J Mol Sci"},{"issue":"5","key":"2024040613390563600_ref66","doi-asserted-by":"crossref","first-page":"657","DOI":"10.14336\/AD.2016.0208","article-title":"Guanosine: a neuromodulator with therapeutic potential in brain disorders","volume":"7","author":"Lanznaster","year":"2016","journal-title":"Aging Dis"},{"issue":"1","key":"2024040613390563600_ref67","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s11302-016-9541-4","article-title":"A Inhibits ATP-induced excitotoxicity: a neuroprotective strategy for traumatic spinal cord injury treatment","volume":"13","author":"Reigada","year":"2017","journal-title":"Purinergic Signal"},{"key":"2024040613390563600_ref68","doi-asserted-by":"crossref","first-page":"646518","DOI":"10.3389\/fnins.2021.646518","article-title":"Roles of selenoproteins in brain function and the potential mechanism of selenium in Alzheimer\u2019s disease","volume":"15","author":"Zhang","year":"2021","journal-title":"Front Neurosci"},{"issue":"1","key":"2024040613390563600_ref69","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.bbrc.2017.01.069","article-title":"Selenomethionine promoted hippocampal neurogenesis via the PI3K-Akt-GSK3\u2013Wnt pathway in a mouse model of Alzheimer\u2019s disease","volume":"485","author":"Zheng","year":"2017","journal-title":"Biochem Biophys Res Commun"},{"issue":"6","key":"2024040613390563600_ref70","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s40261-021-01038-1","article-title":"Riboflavin in neurological diseases: a narrative review","volume":"41","author":"Plantone","year":"2021","journal-title":"Clin Drug Investig"},{"key":"2024040613390563600_ref71","doi-asserted-by":"crossref","first-page":"957605","DOI":"10.1155\/2013\/957605","article-title":"The sulfatase pathway for estrogen formation: targets for the treatment and diagnosis of hormone-associated tumors","volume":"2013","author":"Secky","year":"2013","journal-title":"J Drug Deliv"},{"issue":"3","key":"2024040613390563600_ref72","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s13311-019-00766-9","article-title":"The role of estrogen in brain and cognitive aging","volume":"16","author":"Russell","year":"2019","journal-title":"Neurotherapeutics"},{"key":"2024040613390563600_ref73","doi-asserted-by":"crossref","first-page":"146378","DOI":"10.1016\/j.brainres.2019.146378","article-title":"Cholesterol sulfate alters astrocyte metabolism and provides protection against oxidative stress","volume":"1723","author":"Prah","year":"2019","journal-title":"Brain Res"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae141\/57160132\/bbae141.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/3\/bbae141\/57160132\/bbae141.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T12:51:28Z","timestamp":1731588688000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae141\/7640737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":73,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3,27]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae141","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5]]},"published":{"date-parts":[[2024,3,27]]},"article-number":"bbae141"}}