{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:45:43Z","timestamp":1792266343054,"version":"4.4.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:00:00Z","timestamp":1792195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T00:00:00Z","timestamp":1792195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Funct Integr Genomics"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10142-026-02078-6","type":"journal-article","created":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T08:04:09Z","timestamp":1792224249000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NeuroMetBench: patient-aware evidence bounds for transcript-derived metabolic representations across cancer proteogenomic contexts"],"prefix":"10.1007","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9517-636X","authenticated-orcid":false,"given":"Abdsalam","family":"Bitar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,17]]},"reference":[{"key":"2078_CR1","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","volume":"14","author":"S Aibar","year":"2017","unstructured":"Aibar S, Gonzalez-Blas CB, Moerman T et al (2017) SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14:1083\u20131086. https:\/\/doi.org\/10.1038\/nmeth.4463","journal-title":"Nat Methods"},{"key":"2078_CR2","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1101\/gr.271205.120","volume":"31","author":"N Alghamdi","year":"2021","unstructured":"Alghamdi N, Chang W, Dang P et al (2021) A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res 31:1867\u20131884. https:\/\/doi.org\/10.1101\/gr.271205.120","journal-title":"Genome Res"},{"key":"2078_CR3","doi-asserted-by":"publisher","first-page":"3796","DOI":"10.1016\/j.csbj.2021.06.043","volume":"19","author":"M Andreatta","year":"2021","unstructured":"Andreatta M, Carmona SJ (2021) UCell: robust and scalable single-cell gene signature scoring. Comput Struct Biotechnol J 19:3796\u20133798. https:\/\/doi.org\/10.1016\/j.csbj.2021.06.043","journal-title":"Comput Struct Biotechnol J"},{"key":"2078_CR4","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.3390\/biology15171537","volume":"15","author":"MY Ang","year":"2026","unstructured":"Ang MY, Lipovich L, Choo SW, Chen L, Song L (2026) Trustworthy agentic AI in bioinformatics: from workflow automation to traceable and validated biological inference. Biology 15:1537. https:\/\/doi.org\/10.3390\/biology15171537","journal-title":"Biology"},{"key":"2078_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/nature08460","volume":"462","author":"DA Barbie","year":"2009","unstructured":"Barbie DA, Tamayo P, Boehm JS et al (2009) Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108\u2013112. https:\/\/doi.org\/10.1038\/nature08460","journal-title":"Nature"},{"key":"2078_CR6","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 57:289\u2013300. https:\/\/doi.org\/10.1111\/j.2517-6161.1995.tb02031.x","journal-title":"J Roy Stat Soc B"},{"key":"2078_CR7","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1214\/aos\/1013699998","volume":"29","author":"Y Benjamini","year":"2001","unstructured":"Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165\u20131188. https:\/\/doi.org\/10.1214\/aos\/1013699998","journal-title":"Ann Stat"},{"key":"2078_CR8","doi-asserted-by":"publisher","unstructured":"Broad DepMap (2024). DepMap 24Q4 Public. Figshare+. Dataset. https:\/\/doi.org\/10.25452\/figshare.plus.27993248.v1","DOI":"10.25452\/figshare.plus.27993248.v1"},{"key":"2078_CR9","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1038\/s41576-023-00679-6","volume":"25","author":"TG Brooks","year":"2024","unstructured":"Brooks TG, Lahens NF, Mr\u010dela A, Grant GR (2024) Challenges and best practices in omics benchmarking. Nat Rev Genet 25:326\u2013339. https:\/\/doi.org\/10.1038\/s41576-023-00679-6","journal-title":"Nat Rev Genet"},{"key":"2078_CR10","doi-asserted-by":"publisher","first-page":"bbaf380","DOI":"10.1093\/bib\/bbaf380","volume":"26","author":"Y Cao","year":"2025","unstructured":"Cao Y, Yu L, Torkel M et al (2025) The current landscape and emerging challenges of benchmarking single-cell methods. Brief Bioinform 26:bbaf380. https:\/\/doi.org\/10.1093\/bib\/bbaf380","journal-title":"Brief Bioinform"},{"key":"2078_CR11","doi-asserted-by":"publisher","first-page":"e00090","DOI":"10.1128\/jb.00090-26","volume":"208","author":"J Cao","year":"2026","unstructured":"Cao J, Ye Z, Pan J (2026) Metagenomics for antimicrobial resistance: from resistome surveillance to mechanistic inference. J Bacteriol 208:e00090-26. https:\/\/doi.org\/10.1128\/jb.00090-26","journal-title":"J Bacteriol"},{"key":"2078_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0354354","volume":"21","author":"X Chen","year":"2026","unstructured":"Chen X, Su W, Zhou Q et al (2026) Concordant RNA-protein evidence and human tissue metabolomics prioritize UPP1-associated nucleotide remodeling in prostate cancer. PLoS ONE 21:e0354354. https:\/\/doi.org\/10.1371\/journal.pone.0354354","journal-title":"PLoS ONE"},{"key":"2078_CR13","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1038\/s41467-019-12235-0","volume":"10","author":"D DeTomaso","year":"2019","unstructured":"DeTomaso D, Jones MG, Subramaniam M et al (2019) Functional interpretation of single cell similarity maps. Nat Commun 10:4376. https:\/\/doi.org\/10.1038\/s41467-019-12235-0","journal-title":"Nat Commun"},{"key":"2078_CR14","doi-asserted-by":"publisher","first-page":"1879922","DOI":"10.1155\/humu\/1879922","volume":"2026","author":"C Fang","year":"2026","unstructured":"Fang C, Wu M, Lv J, Zhang Y, Zheng Q, Shen X, Huang S (2026) Human variation-informed prioritization of MPHOSPH6 in lung adenocarcinoma: a source-aware multiomics evidence framework. Hum Mutat 2026:1879922. https:\/\/doi.org\/10.1155\/humu\/1879922","journal-title":"Hum Mutat"},{"key":"2078_CR15","doi-asserted-by":"publisher","unstructured":"Gangwal A, Lavecchia A (2026) From implicit prioritization to auditable decisions in natural product drug discovery. Drug Discovery Today 104804. https:\/\/doi.org\/10.1016\/j.drudis.2026.104804","DOI":"10.1016\/j.drudis.2026.104804"},{"key":"2078_CR16","doi-asserted-by":"publisher","first-page":"pl1","DOI":"10.1126\/scisignal.2004088","volume":"6","author":"J Gao","year":"2013","unstructured":"Gao J, Aksoy BA, Dogrusoz U et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signaling 6:pl1. https:\/\/doi.org\/10.1126\/scisignal.2004088","journal-title":"Sci Signaling"},{"key":"2078_CR17","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1021\/acs.jproteome.5b00852","volume":"15","author":"L Gatto","year":"2016","unstructured":"Gatto L, Hansen KD, Hoopmann MR, Hermjakob H, Kohlbacher O, Beyer A (2016) Testing and validation of computational methods for mass spectrometry. J Proteome Res 15:809\u2013814. https:\/\/doi.org\/10.1021\/acs.jproteome.5b00852","journal-title":"J Proteome Res"},{"key":"2078_CR18","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1471-2105-14-7","volume":"14","author":"S H\u00e4nzelmann","year":"2013","unstructured":"H\u00e4nzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf 14:7. https:\/\/doi.org\/10.1186\/1471-2105-14-7","journal-title":"BMC Bioinf"},{"key":"2078_CR19","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1038\/s41588-021-00791-5","volume":"53","author":"AP Heath","year":"2021","unstructured":"Heath AP, Ferretti V, Agrawal S et al (2021) The NCI genomic data commons. Nat Genet 53:257\u2013262. https:\/\/doi.org\/10.1038\/s41588-021-00791-5","journal-title":"Nat Genet"},{"key":"2078_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2307\/4615733","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65\u201370. https:\/\/doi.org\/10.2307\/4615733","journal-title":"Scand J Stat"},{"key":"2078_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.molmet.2021.101396","volume":"57","author":"K Hrovatin","year":"2022","unstructured":"Hrovatin K, Fischer DS, Theis FJ (2022) Toward modeling metabolic state from single-cell transcriptomics. Molecular Metabolism 57:101396. https:\/\/doi.org\/10.1016\/j.molmet.2021.101396","journal-title":"Molecular Metabolism"},{"key":"2078_CR22","doi-asserted-by":"publisher","first-page":"4883","DOI":"10.1038\/s41467-023-40457-w","volume":"14","author":"Y Huang","year":"2023","unstructured":"Huang Y, Mohanty V, Dede M et al (2023) Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nat Commun 14:4883. https:\/\/doi.org\/10.1038\/s41467-023-40457-w","journal-title":"Nat Commun"},{"key":"2078_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1014700","volume":"22","author":"R Lewandowski","year":"2026","unstructured":"Lewandowski R (2026) The multi-omics fallacy in microbiome science. PLoS Comput Biol 22:e1014700. https:\/\/doi.org\/10.1371\/journal.pcbi.1014700","journal-title":"PLoS Comput Biol"},{"key":"2078_CR24","doi-asserted-by":"publisher","first-page":"bbag200","DOI":"10.1093\/bib\/bbag200","volume":"27","author":"AZ Li","year":"2026","unstructured":"Li AZ, Du Y, Liu Y, Chen L, Liu R (2026) Benchmarking computational methods for multi-omics biomarker discovery in cancer. Brief Bioinform 27:bbag200. https:\/\/doi.org\/10.1093\/bib\/bbag200","journal-title":"Brief Bioinform"},{"key":"2078_CR25","doi-asserted-by":"publisher","first-page":"e70045","DOI":"10.1002\/ggn2.70045","volume":"7","author":"S Lin","year":"2026","unstructured":"Lin S, Yu G (2026) From cluster to claim: calibrating interpretation in single-cell transcriptomics. Adv Genet 7:e70045. https:\/\/doi.org\/10.1002\/ggn2.70045","journal-title":"Adv Genet"},{"key":"2078_CR26","doi-asserted-by":"publisher","DOI":"10.1111\/febs.70680","author":"Y Liu","year":"2026","unstructured":"Liu Y, Westerhoff HV (2026) Metabolic mRNA insufficiency of single cells: implications for drug targeting and resistance. FEBS J. https:\/\/doi.org\/10.1111\/febs.70680","journal-title":"FEBS J"},{"key":"2078_CR27","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.cell.2016.03.014","volume":"165","author":"Y Liu","year":"2016","unstructured":"Liu Y, Beyer A, Aebersold R (2016) On the dependency of cellular protein levels on mRNA abundance. Cell 165:535\u2013550. https:\/\/doi.org\/10.1016\/j.cell.2016.03.014","journal-title":"Cell"},{"key":"2078_CR28","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1016\/j.ccell.2024.06.004","volume":"42","author":"J Liu","year":"2024","unstructured":"Liu J, Cao S, Imbach KJ et al (2024) Multi-scale signaling and tumor evolution in high-grade gliomas. Cancer Cell 42:1217-1238.e19. https:\/\/doi.org\/10.1016\/j.ccell.2024.06.004","journal-title":"Cancer Cell"},{"key":"2078_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1013658","volume":"21","author":"I Mallona","year":"2025","unstructured":"Mallona I, Soneson C, Carrillo B et al (2025) Building a continuous benchmarking ecosystem in bioinformatics. PLoS Comput Biol 21:e1013658. https:\/\/doi.org\/10.1371\/journal.pcbi.1013658","journal-title":"PLoS Comput Biol"},{"key":"2078_CR30","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1038\/s41467-019-09406-4","volume":"10","author":"S Mangul","year":"2019","unstructured":"Mangul S, Martin LS, Hill BL et al (2019) Systematic benchmarking of omics computational tools. Nat Commun 10:1393. https:\/\/doi.org\/10.1038\/s41467-019-09406-4","journal-title":"Nat Commun"},{"key":"2078_CR31","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth JD, Thiele I, Palsson BO (2010) What is flux balance analysis? Nat Biotechnol 28:245\u2013248. https:\/\/doi.org\/10.1038\/nbt.1614","journal-title":"Nat Biotechnol"},{"key":"2078_CR32","doi-asserted-by":"publisher","first-page":"1715377","DOI":"10.3389\/fbinf.2026.1715377","volume":"6","author":"T Pugsley","year":"2026","unstructured":"Pugsley T, Hanke G, Duffy CDP (2026) Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling. Front Bioinform 6:1715377. https:\/\/doi.org\/10.3389\/fbinf.2026.1715377","journal-title":"Front Bioinform"},{"key":"2078_CR33","doi-asserted-by":"publisher","first-page":"3877","DOI":"10.1111\/febs.16787","volume":"290","author":"V Rabattoni","year":"2023","unstructured":"Rabattoni V, Marchesani F, Murtas G et al (2023) The human phosphorylated pathway: a multienzyme metabolic assembly for L-serine biosynthesis. FEBS J 290:3877\u20133895. https:\/\/doi.org\/10.1111\/febs.16787","journal-title":"FEBS J"},{"key":"2078_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.crmeth.2021.100040","volume":"1","author":"A Richelle","year":"2021","unstructured":"Richelle A, Kellman BP, Wenzel AT et al (2021) Model-based assessment of mammalian cell metabolic functionalities using omics data. Cell Reports Methods 1:100040. https:\/\/doi.org\/10.1016\/j.crmeth.2021.100040","journal-title":"Cell Reports Methods"},{"key":"2078_CR35","doi-asserted-by":"publisher","first-page":"eaaz1482","DOI":"10.1126\/scisignal.aaz1482","volume":"13","author":"JL Robinson","year":"2020","unstructured":"Robinson JL, Kocabas P, Wang H et al (2020) An atlas of human metabolism. Sci Signaling 13:eaaz1482. https:\/\/doi.org\/10.1126\/scisignal.aaz1482","journal-title":"Sci Signaling"},{"key":"2078_CR36","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1016\/j.cell.2021.07.016","volume":"184","author":"S Satpathy","year":"2021","unstructured":"Satpathy S, Krug K, Beltran PMJ et al (2021) A proteogenomic portrait of lung squamous cell carcinoma. Cell 184:4348-4371.e40. https:\/\/doi.org\/10.1016\/j.cell.2021.07.016","journal-title":"Cell"},{"key":"2078_CR37","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1038\/s41586-025-09460-7","volume":"646","author":"AJ Scott","year":"2025","unstructured":"Scott AJ, Mittal A, Meghdadi B et al (2025) Rewiring of cortical glucose metabolism fuels human brain cancer growth. Nature 646:413\u2013422. https:\/\/doi.org\/10.1038\/s41586-025-09460-7","journal-title":"Nature"},{"key":"2078_CR38","doi-asserted-by":"publisher","first-page":"lqaf003","DOI":"10.1093\/nargab\/lqaf003","volume":"7","author":"S Sharma","year":"2025","unstructured":"Sharma S, Sauter R, Hotze M et al (2025) GEMCAT - a new algorithm for gene expression-based prediction of metabolic alterations. NAR Genom Bioinform 7:lqaf003. https:\/\/doi.org\/10.1093\/nargab\/lqaf003","journal-title":"NAR Genom Bioinform"},{"key":"2078_CR39","doi-asserted-by":"publisher","first-page":"5692","DOI":"10.1038\/s41467-021-25960-2","volume":"12","author":"JW Squair","year":"2021","unstructured":"Squair JW, Gautier M, Kathe C et al (2021) Confronting false discoveries in single-cell differential expression. Nat Commun 12:5692. https:\/\/doi.org\/10.1038\/s41467-021-25960-2","journal-title":"Nat Commun"},{"key":"2078_CR40","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1186\/s13073-025-01572-z","volume":"17","author":"K Tang","year":"2025","unstructured":"Tang K, Han Y, Sun D et al (2025) Reference-guided computational framework identifies microenvironment metabolic subtypes and targets using pan-cancer single-cell datasets. Genome Med 17:150. https:\/\/doi.org\/10.1186\/s13073-025-01572-z","journal-title":"Genome Med"},{"key":"2078_CR41","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1158\/2767-9764.CRC-24-0243","volume":"4","author":"RR Thangudu","year":"2024","unstructured":"Thangudu RR, Holck M, Singhal D et al (2024) NCI\u2019s Proteomic Data Commons: a cloud-based proteomics repository empowering comprehensive cancer analysis through cross-referencing with genomic and imaging data. Cancer Res Commun 4:2480\u20132488. https:\/\/doi.org\/10.1158\/2767-9764.CRC-24-0243","journal-title":"Cancer Res Commun"},{"key":"2078_CR42","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1038\/s42255-025-01293-y","volume":"7","author":"A Tsyben","year":"2025","unstructured":"Tsyben A, Dannhorn A, Hamm G et al (2025) Cell-intrinsic metabolic phenotypes identified in patients with glioblastoma, using mass spectrometry imaging of 13C-labelled glucose metabolism. Nat Metab 7:928\u2013939. https:\/\/doi.org\/10.1038\/s42255-025-01293-y","journal-title":"Nat Metab"},{"key":"2078_CR43","doi-asserted-by":"publisher","first-page":"4168","DOI":"10.1016\/j.cell.2021.05.045","volume":"184","author":"A Wagner","year":"2021","unstructured":"Wagner A, Wang C, Fessler J et al (2021) Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 184:4168-4185.e21. https:\/\/doi.org\/10.1016\/j.cell.2021.05.045","journal-title":"Cell"},{"key":"2078_CR44","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.ccell.2021.01.006","volume":"39","author":"L-B Wang","year":"2021","unstructured":"Wang L-B, Karpova A, Gritsenko MA et al (2021) Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 39:509-528.e20. https:\/\/doi.org\/10.1016\/j.ccell.2021.01.006","journal-title":"Cancer Cell"},{"key":"2078_CR45","doi-asserted-by":"publisher","first-page":"7458","DOI":"10.1038\/s41467-026-74209-3","volume":"17","author":"H Wang","year":"2026","unstructured":"Wang H, Tang J, Qiao Y, Mu Q, Guo Y, Wang J (2026) T2Pdecoder enables protein-centric analyses from transcriptomic data. Nat Commun 17:7458. https:\/\/doi.org\/10.1038\/s41467-026-74209-3","journal-title":"Nat Commun"},{"key":"2078_CR46","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1158\/2159-8290.CD-21-0316","volume":"12","author":"Y Wu","year":"2022","unstructured":"Wu Y, Yang S, Ma J et al (2022) Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level. Cancer Discov 12:134\u2013153. https:\/\/doi.org\/10.1158\/2159-8290.CD-21-0316","journal-title":"Cancer Discov"},{"key":"2078_CR47","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1038\/s43018-025-00943-0","volume":"6","author":"AX Xie","year":"2025","unstructured":"Xie AX, Tansey W, Reznik E (2025) UnitedMet harnesses RNA-metabolite covariation to impute metabolite levels in clinical samples. Nature Cancer 6:892\u2013906. https:\/\/doi.org\/10.1038\/s43018-025-00943-0","journal-title":"Nature Cancer"},{"key":"2078_CR48","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1038\/s41467-026-68748-y","volume":"17","author":"Z Yang","year":"2026","unstructured":"Yang Z, Wei S, Duan H et al (2026) A proteogenomic atlas of 1032 brain metastases identifies molecular subtypes, immune landscapes, and therapeutic vulnerabilities. Nat Commun 17:2038. https:\/\/doi.org\/10.1038\/s41467-026-68748-y","journal-title":"Nat Commun"},{"key":"2078_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2021.108787","volume":"34","author":"G Yanovich-Arad","year":"2021","unstructured":"Yanovich-Arad G, Ofek P, Yeini E, Mardamshina M, Danilevsky A, Shomron N, Grossman R, Satchi-Fainaro R, Geiger T (2021) Proteogenomics of glioblastoma associates molecular patterns with survival. Cell Rep 34:108787. https:\/\/doi.org\/10.1016\/j.celrep.2021.108787","journal-title":"Cell Rep"},{"key":"2078_CR50","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1038\/s41467-021-21038-1","volume":"12","author":"KD Zimmerman","year":"2021","unstructured":"Zimmerman KD, Espeland MA, Langefeld CD (2021) A practical solution to pseudoreplication bias in single-cell studies. Nat Commun 12:738. https:\/\/doi.org\/10.1038\/s41467-021-21038-1","journal-title":"Nat Commun"}],"container-title":["Functional & Integrative Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10142-026-02078-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10142-026-02078-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10142-026-02078-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:04:10Z","timestamp":1792263850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10142-026-02078-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,17]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2078"],"URL":"https:\/\/doi.org\/10.1007\/s10142-026-02078-6","relation":{},"ISSN":["1438-793X","1438-7948"],"issn-type":[{"value":"1438-793X","type":"print"},{"value":"1438-7948","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,17]]},"assertion":[{"value":"29 August 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was a secondary computational analysis exclusively of publicly available, de-identified datasets and did not recruit participants, collect new human specimens, access identifiable private information, or intervene in patient care. Ethics approvals and informed-consent procedures for the original data collections are reported by the source studies. Because this secondary analysis used only public de-identified data, no additional ethics approval or new informed consent was required for this study.","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable to this secondary computational analysis.","order":3,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable because no identifiable participant information is reported.","order":4,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"292"}}