{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:16:19Z","timestamp":1786122979543,"version":"build-2736575974"},"reference-count":70,"publisher":"American Chemical Society (ACS)","issue":"8","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Graphical networks are useful, widely used modeling approaches to represent complex biological processes with biological measurements generated by platforms such as mass spectrometry. Bayesian analyses of graphical networks for omics data have several advantages over their frequentist counterparts such as the inclusion of prior knowledge in the estimation of models. However, Bayesian approaches to date have only been feasible for data with a couple of hundred biomolecules due to prohibitive computational time, but omics data often contain tens of thousands of biomolecules. Here, we present and illustrate a more computationally efficient approach named BPlane (Bayesian PseudoLikelihood-based Algorithm for Network Estimation) to extend Bayesian modeling capabilities for larger-sized data sets, such as most untargeted proteomics data. Via simulation, we demonstrate that BPlane produces substantial computational savings over a current state-of-the-art Bayesian algorithm while maintaining competitive edge detection accuracy. We further demonstrate the computational benefit of BPlane on a SARS-CoV-2 proteomics data set with 7000 proteins.<\/jats:p>","DOI":"10.1021\/acs.jproteome.5c01277","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:54:47Z","timestamp":1782852887000},"page":"4306-4314","source":"Crossref","is-referenced-by-count":0,"title":["Computationally\nEfficient Bayesian Estimation of Graphical\nNetworks for Omics Data"],"prefix":"10.1021","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4390-8670","authenticated-orcid":true,"given":"Daniel W.","family":"Adrian","sequence":"first","affiliation":[{"name":"Grand Valley State University , , ,","place":["Allendale, Michigan, United States, 49401"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8933-7413","authenticated-orcid":true,"given":"Erik D.","family":"VonKaenel","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moses Y.","family":"Obiri","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5737-7173","authenticated-orcid":true,"given":"David J.","family":"Degnan","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy C.","family":"Sims","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristie","family":"Oxford","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8384-1926","authenticated-orcid":true,"given":"Lisa M.","family":"Bramer","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory , , ,","place":["Richland, Washington, United States, 99354"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"issue":"1","key":"2026080709083536200_cit1","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1055\/s-0038-1667085","article-title":"The Omics Revolution\nContinues: The Maturation of High-Throughput Biological Data Sources","volume":"27","author":"Mathe\u0301","year":"2018","journal-title":"Yearbook Med. Inf."},{"issue":"1","key":"2026080709083536200_cit2","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/nrg2918","article-title":"Network medicine: a network-based\napproach to human disease","volume":"12","author":"Baraba\u0301si","year":"2011","journal-title":"Nat. Rev. Genet."},{"issue":"21","key":"2026080709083536200_cit3","doi-asserted-by":"publisher","first-page":"8685","DOI":"10.1073\/pnas.0701361104","article-title":"The Human\nDisease Network","volume":"104","author":"Goh","year":"2007","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"2026080709083536200_cit4","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1038\/462167a","article-title":"Predicting\npromiscuity","volume":"462","author":"Hopkins","year":"2009","journal-title":"Nature"},{"key":"2026080709083536200_cit5","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1038\/nrd2826","article-title":"A network view of disease and compound\nscreening","volume":"8","author":"Schadt","year":"2009","journal-title":"Nat. Rev. Drug Discovery"},{"key":"2026080709083536200_cit6","doi-asserted-by":"publisher","first-page":"5150","DOI":"10.1002\/sim.9546","article-title":"Gaussian graphical\nmodels with applications to omics analyses","volume":"41","author":"Shutta","year":"2022","journal-title":"Stat. Med."},{"issue":"2","key":"2026080709083536200_cit7","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1021\/acs.jproteome.2c00610","article-title":"pmartR 2.0: A Quality Control, Visualization,\nand Statistics\nPipeline for Multiple Omics Datatypes","volume":"22","author":"Degnan","year":"2023","journal-title":"J. Proteome\nRes."},{"key":"2026080709083536200_cit8","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1021\/acs.jproteome.8b00760","article-title":"pmartR:\nQuality Control and Statistics for Mass Spectrometry-Based Biological\nData","volume":"18","author":"Stratton","year":"2019","journal-title":"J. Proteome Res."},{"key":"2026080709083536200_cit9","volume-title":"Graphical Models","author":"Steffen","year":"1996"},{"issue":"1","key":"2026080709083536200_cit10","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression\nShrinkage and Selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R.\nStat. Soc. Ser. B"},{"issue":"3","key":"2026080709083536200_cit11","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1214\/009053606000000281","article-title":"High-dimensional graphs\nand variable selection with the lasso","volume":"34","author":"Nicolai","year":"2006","journal-title":"Ann.\nStat."},{"issue":"3","key":"2026080709083536200_cit12","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","article-title":"Sparse inverse covariance estimation\nwith the graphical lasso","volume":"9","author":"Friedman","year":"2008","journal-title":"Biostatistics"},{"key":"2026080709083536200_cit13","doi-asserted-by":"publisher","first-page":"2125","DOI":"10.1214\/12-ejs740","article-title":"The graphical lasso:\nNew insights and alternatives","volume":"6","author":"Rahul","year":"2012","journal-title":"Electron. J.\nStat."},{"issue":"4","key":"2026080709083536200_cit14","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1198\/jcgs.2011.11051a","article-title":"New Insights\nand Faster Computations\nfor the Graphical Lasso","volume":"20","author":"Witten","year":"2011","journal-title":"J. Comput. Graphical\nStat."},{"issue":"2","key":"2026080709083536200_cit15","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/rssb.12033","article-title":"The joint\ngraphical lasso for inverse covariance estimation\nacross multiple classes","volume":"76","author":"Danaher","year":"2014","journal-title":"J. R. Stat. Soc., Ser.\nB"},{"issue":"3","key":"2026080709083536200_cit16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.2307\/2987782","article-title":"Statiatical\nanalysis of non-lattice data","volume":"24","author":"Besag","year":"1975","journal-title":"J. R. Stat. Soc.,\nSer. D"},{"key":"2026080709083536200_cit17","volume-title":"A Path Following Algorithm for Sparse\nPseudo-Likelihood Inverse Covariance Estimation","author":"Guilherme","year":"2008"},{"issue":"486","key":"2026080709083536200_cit18","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1198\/jasa.2009.0126","article-title":"Partial Correlation Estimation by\nJoint Sparse Regression Models","volume":"104","author":"Peng","year":"2009","journal-title":"J. Am. Stat.\nAssoc."},{"key":"2026080709083536200_cit19","unstructured":"Jerome, F.; Trevor, H.; Robert, T.\n          Applications of the Lasso\nand Grouped Lasso to the Estimation of Sparse Graphical Models; Stanford University, 2010."},{"issue":"4","key":"2026080709083536200_cit20","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1111\/rssb.12088","article-title":"A convex pseudolikelihood\nframework\nfor high dimensional partial correlation estimation with convergence\nguarantees","volume":"77","author":"Khare","year":"2015","journal-title":"J. R. Stat. Soc., Ser. B"},{"issue":"D1","key":"2026080709083536200_cit21","doi-asserted-by":"publisher","first-page":"D672","DOI":"10.1093\/nar\/gkae909","article-title":"KEGG: biological\nsystems database as a model of the real world","volume":"53","author":"Kanehisa","year":"2025","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026080709083536200_cit22","doi-asserted-by":"publisher","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","article-title":"UniProt: the Universal\nProtein Knowledgebase in 2023","volume":"51","author":"Bateman","year":"2023","journal-title":"Nucleic Acids\nRes."},{"key":"2026080709083536200_cit23","doi-asserted-by":"publisher","first-page":"5395","DOI":"10.1021\/acs.jproteome.4c00535","article-title":"Protein-Protein\nInteraction Networks Derived from Classical and Machine Learning-Based\nNatural Language Processing Tools","volume":"23","author":"Degnan","year":"2024","journal-title":"J. Proteome\nRes."},{"key":"2026080709083536200_cit24","doi-asserted-by":"publisher","first-page":"12195","DOI":"10.1021\/acs.analchem.3c01289","article-title":"malbacR: A Package\nfor Standardized Implementation\nof Batch Correction Methods for Omics Data","volume":"95","author":"Leach","year":"2023","journal-title":"Anal. Chem."},{"issue":"527","key":"2026080709083536200_cit25","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1080\/01621459.2018.1482755","article-title":"Bayesian Regularization\nfor Graphical\nModels With Unequal Shrinkage","volume":"114","author":"Lingrui","year":"2019","journal-title":"J. Am. Stat.\nAssoc."},{"key":"2026080709083536200_cit26","first-page":"1","article-title":"GemBag: Group Estimation\nof Multiple Bayesian Graphical Models","volume":"22","author":"Xinming","year":"2021","journal-title":"J. Mach.\nLearn. Res."},{"key":"2026080709083536200_cit27","volume-title":"The EM Algorithm and\nExtensions","author":"Geoffrey","year":"2007"},{"issue":"548","key":"2026080709083536200_cit28","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.1080\/01621459.2024.2395504","article-title":"Bayesian Structure\nLearning in Undirected Gaussian Graphical Models: Literature Review\nwith Empirical Comparison","volume":"119","author":"Vogels","year":"2024","journal-title":"J. Am. Stat. Assoc."},{"key":"2026080709083536200_cit29","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1080\/01621459.2016.1260469","article-title":"The Spike-and-Slab\nLASSO","volume":"113","author":"Veronika","year":"2018","journal-title":"J.\nAm. Stat. Assoc."},{"issue":"4","key":"2026080709083536200_cit30","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1080\/10618600.2022.2050250","article-title":"The G-Wishart weighted\nproposal algorithm: Efficient posterior computation for Gaussian graphical\nmodels","volume":"31","author":"van den Boom","year":"2022","journal-title":"J. Comput. Graphical Stat."},{"key":"2026080709083536200_cit31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v089.i03","article-title":"BDgraph: An R package for Bayesian structure learning\nin graphical models","volume":"89","author":"Mohammadi","year":"2019","journal-title":"J. Stat Software"},{"issue":"2","key":"2026080709083536200_cit32","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1137\/S003614450342480","article-title":"The\nStructure and Function of Complex Networks","volume":"45","author":"Newman","year":"2003","journal-title":"SIAM Rev."},{"issue":"21","key":"2026080709083536200_cit33","doi-asserted-by":"publisher","first-page":"4947","DOI":"10.1242\/jcs.02714","article-title":"Scale-free\nnetworks in cell biology","volume":"118","author":"Albert","year":"2005","journal-title":"J. Cell Sci."},{"key":"2026080709083536200_cit34","unstructured":"Zhao, T.; Liu, H.; Roeder, K.; Lafferty, J.; Wasserman, L.\n          High-dimensional\nundirected\ngraph estimation\n          Version R package version\n          2011; Vol. 1(5)."},{"issue":"548","key":"2026080709083536200_cit35","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.1080\/01621459.2024.2395504","article-title":"Bayesian\nstructure learning in undirected Gaussian graphical models: Literature\nreview with empirical comparison","volume":"119","author":"Vogels","year":"2024","journal-title":"J. Am. Stat.\nAssoc."},{"key":"2026080709083536200_cit36","unstructured":"Team, R. D. C.\n          \n          R: A Language and Environment\nfor Statistical Computing. 2025."},{"key":"2026080709083536200_cit37","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1385\/1-59259-861-7:183","article-title":"Well-differentiated human airway epithelial cell cultures","volume":"107","author":"Fulcher","year":"2005","journal-title":"Methods Mol. Med."},{"issue":"3","key":"2026080709083536200_cit38","doi-asserted-by":"publisher","DOI":"10.1128\/mSystems.00043-16","article-title":"MPLEx: a Robust and\nUniversal Protocol for Single-Sample Integrative Proteomic, Metabolomic,\nand Lipidomic Analyses","volume":"1","author":"Nakayasu","year":"2016","journal-title":"mSystems"},{"issue":"3","key":"2026080709083536200_cit39","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1039\/C6AN02486F","article-title":"MPLEx:\na method for simultaneous pathogen inactivation\nand extraction of samples for multi-omics profiling","volume":"142","author":"Burnum-Johnson","year":"2017","journal-title":"Analyst"},{"key":"2026080709083536200_cit40","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-1-0716-0211-9_14","article-title":"Metabolite, Protein,\nand Lipid Extraction (MPLEx): A Method that Simultaneously Inactivates\nMiddle East Respiratory Syndrome Coronavirus and Allows Analysis of\nMultiple Host Cell Components Following Infection","volume":"2099","author":"Nicora","year":"2020","journal-title":"Methods Mol. Biol."},{"issue":"5","key":"2026080709083536200_cit41","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1021\/pr301146m","article-title":"Sources of technical\nvariability in quantitative LC-MS proteomics: human brain tissue sample\nanalysis","volume":"12","author":"Piehowski","year":"2013","journal-title":"J. Proteome Res."},{"issue":"19","key":"2026080709083536200_cit42","doi-asserted-by":"publisher","first-page":"12660","DOI":"10.1021\/acsomega.1c00776","article-title":"Assessment\nof TMT Labeling Efficiency in Large-Scale Quantitative Proteomics:\nThe Critical Effect of Sample pH","volume":"6","author":"Hutchinson-Bunch","year":"2021","journal-title":"ACS Omega"},{"issue":"2\u20133","key":"2026080709083536200_cit43","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0014-2999(98)00768-7","article-title":"Comparison of the\nvascular relaxant effects of ATP-dependent K+ channel openers on aorta\nand pulmonary artery isolated from spontaneously hypertensive and\nWistar-Kyoto rats","volume":"365","author":"Kwan","year":"1999","journal-title":"Eur. J. Pharmacol."},{"issue":"23","key":"2026080709083536200_cit44","doi-asserted-by":"publisher","first-page":"3838","DOI":"10.1093\/bioinformatics\/btv437","article-title":"Correcting\nsystematic bias and instrument measurement drift with mzRefinery","volume":"31","author":"Gibbons","year":"2015","journal-title":"Bioinformatics"},{"key":"2026080709083536200_cit45","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms6277","article-title":"MS-GF+ makes progress\ntowards a universal database\nsearch tool for proteomics","volume":"5","author":"Kim","year":"2014","journal-title":"Nat. Commun."},{"issue":"3","key":"2026080709083536200_cit46","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.compbiolchem.2008.02.006","article-title":"MASIC:\na software program for fast quantitation and flexible visualization\nof chromatographic profiles from detected LC-MS(\/MS) features","volume":"32","author":"Monroe","year":"2008","journal-title":"Comput. Biol. Chem."},{"key":"2026080709083536200_cit47","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1021\/pr501138h","article-title":"Review, Evaluation,\nand Discussion of the Challenges of Missing Value Imputation for Mass\nSpectrometry-Based Label-Free Global Proteomics","volume":"14","author":"Webb-Robertson","year":"2015","journal-title":"J. Proteome Res."},{"key":"2026080709083536200_cit48","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-026-70118-7","article-title":"Analysis\nof isobaric quantitative proteomic data using\nTMT-Integrator and FragPipe computational platform","volume":"17","author":"Chang","year":"2026","journal-title":"Nat. Commun."},{"issue":"23\u201324","key":"2026080709083536200_cit49","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.202200092","article-title":"Dealing with\nmissing values in proteomics data","volume":"22","author":"Kong","year":"2022","journal-title":"Proteomics"},{"issue":"1","key":"2026080709083536200_cit50","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.cell.2023.12.004","article-title":"Proteogenomic characterization of small cell lung cancer identifies\nbiological insights and subtype-specific therapeutic strategies","volume":"187","author":"Liu","year":"2024","journal-title":"Cell"},{"key":"2026080709083536200_cit51","doi-asserted-by":"publisher","DOI":"10.1016\/j.redox.2020.101491","article-title":"Comprehensive\nchemical proteomics for target deconvolution of the redox active drug\nauranofin","volume":"32","author":"Saei","year":"2020","journal-title":"Redox Biol."},{"issue":"3","key":"2026080709083536200_cit52","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s42485-024-00166-4","article-title":"Interrogating\ndata-independent acquisition LC-MS\/MS for affinity proteomics","volume":"15","author":"Tabb","year":"2024","journal-title":"J. Proteins Proteom"},{"issue":"D1","key":"2026080709083536200_cit53","doi-asserted-by":"publisher","first-page":"D648","DOI":"10.1093\/nar\/gkab1006","article-title":"The IntAct database: efficient access to fine-grained\nmolecular interaction data","volume":"50","author":"Del Toro","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"2026080709083536200_cit54","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1214\/12-ejs669","article-title":"Efficient Gaussian\ngraphical model determination under G-Wishart\nprior distributions","volume":"6","author":"Wang","year":"2012","journal-title":"Electron. J. Statist."},{"issue":"7","key":"2026080709083536200_cit55","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0253364","article-title":"Biochemical characterization\nof protease activity of Nsp3 from SARS-CoV-2 and its inhibition by\nnanobodies","volume":"16","author":"Armstrong","year":"2021","journal-title":"PLoS One"},{"issue":"1","key":"2026080709083536200_cit56","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbc.2021.100821","article-title":"Arginine methylation\nof SARS-Cov-2 nucleocapsid protein regulates RNA binding, its ability\nto suppress stress granule formation, and viral replication","volume":"297","author":"Cai","year":"2021","journal-title":"J. Biol. Chem."},{"issue":"1","key":"2026080709083536200_cit57","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-25589-1","article-title":"Sequences\nin the cytoplasmic tail of SARS-CoV-2 Spike facilitate\nexpression at the cell surface and syncytia formation","volume":"12","author":"Cattin-Ortola\u0301","year":"2021","journal-title":"Nat. Commun."},{"issue":"17","key":"2026080709083536200_cit58","doi-asserted-by":"publisher","DOI":"10.15252\/embj.2021107776","article-title":"Interactomes of SARS-CoV-2 and human coronaviruses reveal host factors\npotentially affecting pathogenesis","volume":"40","author":"Chen","year":"2021","journal-title":"EMBO J."},{"issue":"8","key":"2026080709083536200_cit59","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1021\/acsinfecdis.0c00168","article-title":"Characterization\nand noncovalent inhibition of the deubiquitinase and deISGylase activity\nof SARS-CoV-2 papain-like protease","volume":"6","author":"Freitas","year":"2020","journal-title":"ACS Infect.\nDis."},{"issue":"1","key":"2026080709083536200_cit60","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-33018-0","article-title":"Quantitative fragmentomics\nallow affinity mapping of interactomes","volume":"13","author":"Gogl","year":"2022","journal-title":"Nat.\nCommun."},{"issue":"6521","key":"2026080709083536200_cit61","doi-asserted-by":"publisher","DOI":"10.1126\/science.abe9403","article-title":"Comparative\nhost-coronavirus protein interaction networks\nreveal pan-viral disease mechanisms","volume":"370","author":"Gordon","year":"2020","journal-title":"Science"},{"issue":"7816","key":"2026080709083536200_cit62","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1038\/s41586-020-2286-9","article-title":"A SARS-CoV-2\nprotein interaction map reveals\ntargets for drug repurposing","volume":"583","author":"Gordon","year":"2020","journal-title":"Nature"},{"issue":"10","key":"2026080709083536200_cit63","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22105336","article-title":"Contribution of syndecans\nto the cellular entry of SARS-CoV-2","volume":"22","author":"Huda\u0301k","year":"2021","journal-title":"Int. J.\nMol. Sci."},{"issue":"1","key":"2026080709083536200_cit64","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.medj.2020.07.002","article-title":"Virus-host\ninteractome and proteomic survey reveal potential virulence factors\ninfluencing SARS-CoV-2 pathogenesis","volume":"2","author":"Li","year":"2021","journal-title":"Med"},{"issue":"1","key":"2026080709083536200_cit65","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2021.103562","article-title":"SARS-CoV-2\nnucleocapsid\nprotein binds host mRNAs and attenuates stress granules to impair\nhost stress response","volume":"25","author":"Nabeel-Shah","year":"2022","journal-title":"iScience"},{"issue":"7835","key":"2026080709083536200_cit66","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1038\/s41586-020-2601-5","article-title":"Papain-like protease regulates SARS-CoV-2 viral spread\nand innate immunity","volume":"587","author":"Shin","year":"2020","journal-title":"Nature"},{"issue":"7862","key":"2026080709083536200_cit67","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1038\/s41586-021-03493-4","article-title":"Multilevel\nproteomics reveals host perturbations by\nSARS-CoV-2 and SARS-CoV","volume":"594","author":"Stukalov","year":"2021","journal-title":"Nature"},{"issue":"9","key":"2026080709083536200_cit68","doi-asserted-by":"publisher","DOI":"10.3390\/pathogens10091155","article-title":"Interactome analysis\nof the nucleocapsid protein of SARS-CoV-2 virus","volume":"10","author":"Zheng","year":"2021","journal-title":"Pathogens"},{"issue":"9","key":"2026080709083536200_cit69","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1093\/abbs\/gmab088","article-title":"Nsp2 has the potential to be a drug\ntarget revealed\nby global identification of SARS-CoV-2 Nsp2-interacting proteins","volume":"53","author":"Zheng","year":"2021","journal-title":"Acta Biochim. Biophys. Sin."},{"issue":"1","key":"2026080709083536200_cit70","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1038\/s41587-022-01474-0","article-title":"A comprehensive SARS-CoV-2\u2013human protein\u2013protein interactome\nreveals COVID-19 pathobiology and potential host therapeutic targets","volume":"41","author":"Zhou","year":"2023","journal-title":"Nat. Biotechnol."}],"container-title":["Journal of Proteome Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jproteome.5c01277","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jprobs\/article-pdf\/25\/8\/4306\/65298215\/acs.jproteome.5c01277.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jprobs\/article-pdf\/25\/8\/4306\/65298215\/acs.jproteome.5c01277.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T13:21:14Z","timestamp":1786108874000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jprobs\/article\/25\/8\/4306\/5171814\/Computationally-Efficient-Bayesian-Estimation-of"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":70,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,6,30]]},"published-print":{"date-parts":[[2026,8,7]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jproteome.5c01277","relation":{},"ISSN":["1535-3893","1535-3907"],"issn-type":[{"value":"1535-3893","type":"print"},{"value":"1535-3907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,30]]}}}