{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:49:17Z","timestamp":1792698557830,"version":"4.5.0"},"reference-count":56,"publisher":"Elsevier BV","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T00:00:00Z","timestamp":1744588800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006132","name":"Office of Science","doi-asserted-by":"publisher","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}],"id":[{"id":"10.13039\/100006132","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIH National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01 GM126553"],"award-info":[{"award-number":["R01 GM126553"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R01 HG011883"],"award-info":[{"award-number":["R01 HG011883"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000027","name":"National Institute on Alcohol Abuse and Alcoholism","doi-asserted-by":"publisher","award":["R01AA029688"],"award-info":[{"award-number":["R01AA029688"]}],"id":[{"id":"10.13039\/100000027","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016220","name":"Georgia Clinical and Translational Science Alliance","doi-asserted-by":"publisher","award":["2U54TR002389-06"],"award-info":[{"award-number":["2U54TR002389-06"]}],"id":[{"id":"10.13039\/100016220","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017618","name":"National Institute of Diabetes and Digestive and Kidney Diseases Division of Diabetes Endocrinology and Metabolic Diseases","doi-asserted-by":"publisher","award":["P30DK020595"],"award-info":[{"award-number":["P30DK020595"]}],"id":[{"id":"10.13039\/100017618","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017045","name":"Research Computing Center, University of Chicago","doi-asserted-by":"publisher","award":["Beagle3"],"award-info":[{"award-number":["Beagle3"]}],"id":[{"id":"10.13039\/100017045","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007234","name":"University of Chicago","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007234","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Cell Genomics"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.xgen.2025.100875","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T10:35:36Z","timestamp":1747218936000},"page":"100875","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"title":["scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework"],"prefix":"10.1016","volume":"5","author":[{"given":"Yichao","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Temidayo","family":"Adeluwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisha","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia","family":"Salazar-Maga\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah","family":"Sumner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunki","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saideep","family":"Gona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Festus","family":"Nyasimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph E.","family":"Powell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Madduri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boxiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0333-5685","authenticated-orcid":false,"given":"Hae Kyung","family":"Im","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xgen.2025.100875_bib1","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1038\/ng.3367","article-title":"A gene-based association method for mapping traits using reference transcriptome data","volume":"47","author":"Gamazon","year":"2015","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/ng.3506","article-title":"Integrative approaches for large-scale transcriptome-wide association studies","volume":"48","author":"Gusev","year":"2016","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib3","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/s41588-019-0385-z","article-title":"Opportunities and challenges for transcriptome-wide association studies","volume":"51","author":"Wainberg","year":"2019","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib4","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.ajhg.2017.01.031","article-title":"Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits","volume":"100","author":"Mancuso","year":"2017","journal-title":"Am. J. Hum. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib5","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1038\/s41588-018-0092-1","article-title":"Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights","volume":"50","author":"Gusev","year":"2018","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib6","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.tig.2020.08.009","article-title":"Where Are the Disease-Associated eQTLs?","volume":"37","author":"Umans","year":"2021","journal-title":"Trends Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib7","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1038\/s41467-022-30893-5","article-title":"Single-cell RNA-sequencing of peripheral blood mononuclear cells reveals widespread, context-specific gene expression regulation upon pathogenic exposure","volume":"13","author":"Oelen","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.xgen.2025.100875_bib8","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1038\/nature12172","article-title":"Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells","volume":"498","author":"Shalek","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.xgen.2025.100875_bib9","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1038\/s41592-021-01252-x","article-title":"Effective gene expression prediction from sequence by integrating long-range interactions","volume":"18","author":"Avsec","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.xgen.2025.100875_bib10","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/s41588-021-00782-6","article-title":"Base-resolution models of transcription-factor binding reveal soft motif syntax","volume":"53","author":"Avsec","year":"2021","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib11","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1038\/s41588-018-0160-6","article-title":"Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk","volume":"50","author":"Zhou","year":"2018","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib12","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1038\/s41467-018-03621-1","article-title":"Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics","volume":"9","author":"Barbeira","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.xgen.2025.100875_bib13","doi-asserted-by":"crossref","DOI":"10.1126\/science.abf3041","article-title":"Single-cell eQTL mapping identifies cell type\u2013specific genetic control of autoimmune disease","volume":"376","author":"Yazar","year":"2022","journal-title":"Science"},{"key":"10.1016\/j.xgen.2025.100875_bib14","article-title":"The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans","volume":"376","author":"Jones","year":"2022","journal-title":"Science"},{"key":"10.1016\/j.xgen.2025.100875_bib15","article-title":"Single-cell gene expression prediction from DNA sequence at large contexts","author":"Schwessinger","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.xgen.2025.100875_bib16","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1038\/s41588-023-01524-6","article-title":"Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings","volume":"55","author":"Sasse","year":"2023","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib17","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1038\/s41588-023-01574-w","article-title":"Personal transcriptome variation is poorly explained by current genomic deep learning models","volume":"55","author":"Huang","year":"2023","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib18","doi-asserted-by":"crossref","first-page":"9440","DOI":"10.1073\/pnas.1530509100","article-title":"Statistical significance for genomewide studies","volume":"100","author":"Storey","year":"2003","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.xgen.2025.100875_bib19","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nature24277","article-title":"Genetic effects on gene expression across human tissues","volume":"550","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.xgen.2025.100875_bib20","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1038\/nature15393","article-title":"A global reference for human genetic variation","volume":"526","author":"Auton","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.xgen.2025.100875_bib21","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.ajhg.2019.01.002","article-title":"ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies","volume":"104","author":"Liu","year":"2019","journal-title":"Am. J. Hum. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib22","article-title":"On the problem of inflation in transcriptome-wide association studies","author":"Liang","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.xgen.2025.100875_bib23","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1093\/bioinformatics\/btv546","article-title":"Approximately independent linkage disequilibrium blocks in human populations","volume":"32","author":"Berisa","year":"2016","journal-title":"Bioinformatics"},{"key":"10.1016\/j.xgen.2025.100875_bib24","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41588-019-0345-7","article-title":"A statistical framework for cross-tissue transcriptome-wide association analysis","volume":"51","author":"Li","year":"2019","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib25","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.cmet.2023.03.001","article-title":"The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits","volume":"35","author":"Costanzo","year":"2023","journal-title":"Cell Metab."},{"key":"10.1016\/j.xgen.2025.100875_bib26","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2016.00395","article-title":"Calcium Sensing Receptor as a Novel Mediator of Adipose Tissue Dysfunction: Mechanisms and Potential Clinical Implications","volume":"7","author":"Bravo-Sagua","year":"2016","journal-title":"Front. Physiol."},{"key":"10.1016\/j.xgen.2025.100875_bib27","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0139844","article-title":"MsrA Overexpression Targeted to the Mitochondria, but Not Cytosol, Preserves Insulin Sensitivity in Diet-Induced Obese Mice","volume":"10","author":"Hunnicut","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.xgen.2025.100875_bib28","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1093\/eurheartj\/ehaa972","article-title":"Metabolic profiling of angiopoietin-like protein 3 and 4 inhibition: a drug-target Mendelian randomization analysis","volume":"42","author":"Wang","year":"2021","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.xgen.2025.100875_bib29","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1007\/s00439-022-02434-z","article-title":"An effector index to predict target genes at GWAS loci","volume":"141","author":"Forgetta","year":"2022","journal-title":"Hum. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib30","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1038\/s41588-023-01443-6","article-title":"Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases","volume":"55","author":"Weeks","year":"2023","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib31","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1016\/j.cmet.2022.08.014","article-title":"3D chromatin maps of the human pancreas reveal lineage-specific regulatory architecture of T2D risk","volume":"34","author":"Su","year":"2022","journal-title":"Cell Metab."},{"key":"10.1016\/j.xgen.2025.100875_bib32","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41588-022-01261-2","article-title":"A genome-wide CRISPR screen identifies CALCOCO2 as a regulator of beta cell function influencing type 2 diabetes risk","volume":"55","author":"Rottner","year":"2023","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib33","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1038\/s41588-022-01058-3","article-title":"Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation","volume":"54","author":"Mahajan","year":"2022","journal-title":"Nat. Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib34","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1004383","article-title":"Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics","volume":"10","author":"Giambartolomei","year":"2014","journal-title":"PLoS Genet."},{"key":"10.1016\/j.xgen.2025.100875_bib35","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1136\/annrheumdis-2014-205496","article-title":"NLRP3 and ASC suppress lupus-like autoimmunity by driving the immunosuppressive effects of TGF-\u03b2 receptor signalling","volume":"74","author":"Lech","year":"2015","journal-title":"Ann. Rheum. Dis."},{"key":"10.1016\/j.xgen.2025.100875_bib36","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1056\/NEJMoa0707865","article-title":"Association of Systemic Lupus Erythematosus with C8orf13\u2013BLK and ITGAM\u2013ITGAX","volume":"358","author":"Hom","year":"2008","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.xgen.2025.100875_bib37","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1002\/art.42270","article-title":"Strong Association of Combined Genetic Deficiencies in the Classical Complement Pathway With Risk of Systemic Lupus Erythematosus and Primary Sj\u00f6gren\u2019s Syndrome","volume":"74","author":"Lundtoft","year":"2022","journal-title":"Arthritis Rheumatol."},{"key":"10.1016\/j.xgen.2025.100875_bib38","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2016.00055","article-title":"Systemic Lupus Erythematosus and Deficiencies of Early Components of the Complement Classical Pathway","volume":"7","author":"Macedo","year":"2016","journal-title":"Front. Immunol."},{"key":"10.1016\/j.xgen.2025.100875_bib39","doi-asserted-by":"crossref","DOI":"10.3389\/fimmu.2016.00035","article-title":"Clearance Deficiency and Cell Death Pathways: A Model for the Pathogenesis of SLE","volume":"7","author":"Mahajan","year":"2016","journal-title":"Front. Immunol."},{"key":"10.1016\/j.xgen.2025.100875_bib40","doi-asserted-by":"crossref","first-page":"626","DOI":"10.3390\/jcm10040626","article-title":"Expanding the Role of Complement Therapies: The Case for Lupus Nephritis","volume":"10","author":"Li","year":"2021","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.xgen.2025.100875_bib41","doi-asserted-by":"crossref","first-page":"e32","DOI":"10.1136\/annrheumdis-2014-207207","article-title":"Persistent expression of CXCR5 on plasmablasts in IgG4-related disease","volume":"74","author":"Koarada","year":"2015","journal-title":"Ann. Rheum. Dis."},{"key":"10.1016\/j.xgen.2025.100875_bib42","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1111\/cei.12791","article-title":"CXCR5 is critically involved in progression of lupus through regulation of B cell and double-negative T cell trafficking","volume":"185","author":"Wiener","year":"2016","journal-title":"Clin. Exp. Immunol."},{"key":"10.1016\/j.xgen.2025.100875_bib43","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13059-023-02899-9","article-title":"Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers","volume":"24","author":"Karollus","year":"2023","journal-title":"Genome Biol."},{"key":"10.1016\/j.xgen.2025.100875_bib44","article-title":"A Unified Approach to Interpreting Model Predictions","author":"Lundberg","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.xgen.2025.100875_bib45","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/nbt.4042","article-title":"Multiplexed droplet single-cell RNA-sequencing using natural genetic variation","volume":"36","author":"Kang","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.xgen.2025.100875_bib46","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cels.2018.11.005","article-title":"Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data","volume":"8","author":"Wolock","year":"2019","journal-title":"Cell Syst."},{"key":"10.1016\/j.xgen.2025.100875_bib47","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1186\/s13059-019-1874-1","article-title":"Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression","volume":"20","author":"Hafemeister","year":"2019","journal-title":"Genome Biol."},{"key":"10.1016\/j.xgen.2025.100875_bib48","doi-asserted-by":"crossref","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat. Commun."},{"key":"10.1016\/j.xgen.2025.100875_bib49","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1016\/j.celrep.2019.09.082","article-title":"DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data","volume":"29","author":"DePasquale","year":"2019","journal-title":"Cell Rep."},{"key":"10.1016\/j.xgen.2025.100875_bib50","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13059-020-1950-6","article-title":"Decontamination of ambient RNA in single-cell RNA-seq with DecontX","volume":"21","author":"Yang","year":"2020","journal-title":"Genome Biol."},{"key":"10.1016\/j.xgen.2025.100875_bib51","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.xgen.2025.100875_bib52","doi-asserted-by":"crossref","DOI":"10.15252\/msb.20209620","article-title":"Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models","volume":"17","author":"Xu","year":"2021","journal-title":"Mol. Syst. Biol."},{"key":"10.1016\/j.xgen.2025.100875_bib53","doi-asserted-by":"crossref","first-page":"5556","DOI":"10.1038\/s41467-021-25725-x","article-title":"Leveraging the Cell Ontology to classify unseen cell types","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.xgen.2025.100875_bib54","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat. Methods"},{"key":"10.1016\/j.xgen.2025.100875_bib55","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1093\/bioinformatics\/btz625","article-title":"BBKNN: fast batch alignment of single cell transcriptomes","volume":"36","author":"Pola\u0144ski","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.xgen.2025.100875_bib56","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1038\/s41587-019-0113-3","article-title":"Efficient integration of heterogeneous single-cell transcriptomes using Scanorama","volume":"37","author":"Hie","year":"2019","journal-title":"Nat. Biotechnol."}],"container-title":["Cell Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666979X25001314?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666979X25001314?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T22:30:59Z","timestamp":1762641059000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666979X25001314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":56,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["S2666979X25001314"],"URL":"https:\/\/doi.org\/10.1016\/j.xgen.2025.100875","relation":{},"ISSN":["2666-979X"],"issn-type":[{"value":"2666-979X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"scPrediXcan integrates deep learning methods and single-cell data into a cell-type-specific transcriptome-wide association study framework","name":"articletitle","label":"Article Title"},{"value":"Cell Genomics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xgen.2025.100875","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100875"}}