{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:23:03Z","timestamp":1777119783128,"version":"3.51.4"},"reference-count":80,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":9,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"NSF CAREER","award":["DBI-2239350"],"award-info":[{"award-number":["DBI-2239350"]}]},{"DOI":"10.13039\/501100006606","name":"Natural Science Foundation of Tianjin City","doi-asserted-by":"publisher","award":["19JCZDJC35100"],"award-info":[{"award-number":["19JCZDJC35100"]}],"id":[{"id":"10.13039\/501100006606","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61572358"],"award-info":[{"award-number":["61572358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The development of single-cell RNA sequencing (scRNA-seq) technology provides valuable data resources for inferring gene regulatory networks (GRNs), enabling deeper insights into cellular mechanisms and diseases. While many methods exist for inferring GRNs from static scRNA-seq data, current approaches face challenges in accurately handling time-series scRNA-seq data due to high noise levels and data sparsity. The temporal dimension introduces additional complexity by requiring models to capture dynamic changes, increasing sensitivity to noise, and exacerbating data sparsity across time points. In this study, we introduce GRANGER, an unsupervised deep learning-based method that integrates multiple advanced techniques, including a recurrent variational autoencoder, GRANGER causality, sparsity-inducing penalties, and negative binomial (NB)-based loss functions, to infer GRNs. GRANGER was evaluated using multiple popular benchmarking datasets, where it demonstrated superior performance compared to eight well-known GRN inference methods. The integration of a NB-based loss function and sparsity-inducing penalties in GRANGER significantly enhanced its capacity to address dropout noise and sparsity in scRNA-seq data. Additionally, GRANGER exhibited robustness against high levels of dropout noise. We applied GRANGER to scRNA-seq data from the whole mouse brain obtained through the BRAIN Initiative project and identified GRNs for five transcription regulators: E2f7, Gbx1, Sox10, Prox1, and Onecut2, which play crucial roles in diverse brain cell types. The inferred GRNs not only recalled many known regulatory relationships but also revealed sets of novel regulatory interactions with functional potential. These findings demonstrate that GRANGER is a highly effective tool for real-world applications in discovering novel gene regulatory relationships.<\/jats:p>","DOI":"10.1093\/bib\/bbaf089","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T10:54:36Z","timestamp":1741604076000},"source":"Crossref","is-referenced-by-count":10,"title":["Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders"],"prefix":"10.1093","volume":"26","author":[{"given":"Liang","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University , 393 Binshui W Ave, Tianjin, Tianjin 300387 ,","place":["China"]}]},{"given":"Madison","family":"Dautle","sequence":"additional","affiliation":[{"name":"Department of Biological and Biomedical Sciences, Rowan University , 201 Mullica Hill Road, Glassboro, NJ 08028 ,","place":["United States"]}]},{"given":"Ruoying","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University , 393 Binshui W Ave, Tianjin, Tianjin 300387 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4127-0539","authenticated-orcid":false,"given":"Shaoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University , 393 Binshui W Ave, Tianjin, Tianjin 300387 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-4321","authenticated-orcid":false,"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biological and Biomedical Sciences, Rowan University , 201 Mullica Hill Road, Glassboro, NJ 08028 ,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"2025031010541706500_ref1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.3389\/fcell.2014.00038","article-title":"Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks","volume":"2","author":"Emmert-Streib","year":"2014","journal-title":"Front Cell Dev Biol"},{"key":"2025031010541706500_ref2","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s13073-018-0608-4","article-title":"An integrative approach for building personalized gene regulatory networks for precision medicine","volume":"10","author":"Wijst","year":"2018","journal-title":"Genome Med"},{"key":"2025031010541706500_ref3","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/s41576-019-0200-9","article-title":"Mechanisms of tissue and cell-type specificity in heritable traits and diseases","volume":"21","author":"Hekselman","year":"2020","journal-title":"Nat Rev Genet"},{"key":"2025031010541706500_ref4","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1093\/bfgp\/elx046","article-title":"Mapping gene regulatory networks from single-cell omics data","volume":"17","author":"Fiers","year":"2018","journal-title":"Brief Funct Genomics"},{"key":"2025031010541706500_ref5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13258-023-01473-8","article-title":"A review on gene regulatory network reconstruction algorithms based on single cell RNA sequencing","volume":"46","author":"Kim","year":"2024","journal-title":"Genes Genomics"},{"key":"2025031010541706500_ref6","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae267","article-title":"Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data","volume":"40","author":"Stock","year":"2024","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref7","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa190","article-title":"A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data","volume":"22","author":"Nguyen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref8","doi-asserted-by":"publisher","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PloS One"},{"key":"2025031010541706500_ref9","doi-asserted-by":"publisher","first-page":"2159","DOI":"10.1093\/bioinformatics\/bty916","article-title":"GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks","volume":"35","author":"Moerman","year":"2019","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref10","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2025031010541706500_ref11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0158247","article-title":"Nonlinear network reconstruction from gene expression data using marginal dependencies measured by DCOL","volume":"11","author":"Liu","year":"2016","journal-title":"PloS One"},{"key":"2025031010541706500_ref12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cels.2017.08.014","article-title":"Gene regulatory network inference from single-cell data using multivariate information measures","volume":"5","author":"Chan","year":"2017","journal-title":"Cell Syst"},{"key":"2025031010541706500_ref13","doi-asserted-by":"publisher","author":"Kim","DOI":"10.5351\/CSAM.2015.22.6.665"},{"key":"2025031010541706500_ref14","doi-asserted-by":"crossref","first-page":"e1004575","DOI":"10.1371\/journal.pcbi.1004575","article-title":"SINCERA: a pipeline for single-cell RNA-Seq profiling analysis","volume":"11","author":"Guo","year":"2015","journal-title":"PLoS Comput Biol"},{"key":"2025031010541706500_ref15","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/bioinformatics\/btw729","article-title":"LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering","volume":"33","author":"Specht","year":"2017","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref16","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1093\/bioinformatics\/btx605","article-title":"A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data","volume":"34","author":"Sanchez-Castillo","year":"2018","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref17","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1093\/bioinformatics\/btx575","article-title":"SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles","volume":"34","author":"Papili Gao","year":"2018","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref18","first-page":"576","article-title":"Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories","volume":"22","author":"Cordero","year":"2017","journal-title":"Pac Symp Biocomput"},{"key":"2025031010541706500_ref19"},{"key":"2025031010541706500_ref20","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad326","article-title":"Inferring single-cell gene regulatory network by non-redundant mutual information","volume":"24","author":"Zeng","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref21","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.cels.2024.04.005","article-title":"Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors","volume":"15","author":"Singh","year":"2024","journal-title":"Cell Syst"},{"key":"2025031010541706500_ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2022.110333","article-title":"Network inference with GRANGER causality ensembles on single-cell transcriptomics","volume":"38","author":"Deshpande","year":"2022","journal-title":"Cell Rep"},{"key":"2025031010541706500_ref23","doi-asserted-by":"publisher","DOI":"10.3390\/ijms241713339","article-title":"scTIGER: a deep-learning method for inferring gene regulatory networks from case versus control scRNA-seq datasets","volume":"24","author":"Dautle","year":"2023","journal-title":"Int J Mol Sci"},{"key":"2025031010541706500_ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab166","article-title":"MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction","volume":"22","author":"He","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref25","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1038\/s41598-018-21715-0","article-title":"dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data","volume":"8","author":"Huynh-Thu","year":"2018","journal-title":"Sci Rep"},{"key":"2025031010541706500_ref26","doi-asserted-by":"publisher","first-page":"i89","DOI":"10.1093\/bioinformatics\/btv257","article-title":"Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data","volume":"31","author":"Ocone","year":"2015","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref27","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1093\/bioinformatics\/btx194","article-title":"SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation","volume":"33","author":"Matsumoto","year":"2017","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref28","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1186\/s12859-016-1109-3","article-title":"SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation","volume":"17","author":"Matsumoto","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2025031010541706500_ref29","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.1093\/bioinformatics\/btaa576","article-title":"Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference","volume":"36","author":"Aubin-Frankowski","year":"2020","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref30","doi-asserted-by":"crossref","first-page":"27151","DOI":"10.1073\/pnas.1911536116","article-title":"Deep learning for inferring gene relationships from single-cell expression data","volume":"116","author":"Yuan","year":"2019","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025031010541706500_ref31","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab325","article-title":"DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref32","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac424","article-title":"dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data","volume":"23","author":"Xu","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref33","doi-asserted-by":"crossref","first-page":"4522","DOI":"10.1093\/bioinformatics\/btac559","article-title":"Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data","volume":"38","author":"Chen","year":"2022","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref34","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad414","article-title":"Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks","volume":"24","author":"Mao","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref35","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab142","article-title":"Deep learning of gene relationships from single cell time-course expression data","volume":"22","author":"Yuan","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103656","article-title":"SCGRNs: novel supervised inference of single-cell gene regulatory networks of complex diseases","volume":"118","author":"Turki","year":"2020","journal-title":"Comput Biol Med"},{"key":"2025031010541706500_ref37","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad165","article-title":"STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data","volume":"39","author":"Xu","year":"2023","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref38","doi-asserted-by":"publisher","first-page":"766","DOI":"10.3390\/biom14070766","article-title":"DeepIMAGER: deeply Analyzing gene regulatory networks from scRNA-seq data","volume":"14","author":"Zhou","year":"2024","journal-title":"Biomolecules"},{"key":"2025031010541706500_ref39","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkac1212","article-title":"Single-cell gene regulatory network prediction by explainable AI","volume":"51","author":"Keyl","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2025031010541706500_ref40","doi-asserted-by":"publisher","first-page":"vbad003","DOI":"10.1093\/bioadv\/vbad003","article-title":"scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference","volume":"3","author":"Li","year":"2023","journal-title":"Bioinform Adv"},{"key":"2025031010541706500_ref41","doi-asserted-by":"publisher","first-page":"1368","DOI":"10.1038\/s41592-023-01971-3","article-title":"Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics","volume":"20","author":"Wang","year":"2023","journal-title":"Nat Methods"},{"key":"2025031010541706500_ref42","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-024-02182-7","article-title":"Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data","volume":"43","author":"Yuan","year":"2025","journal-title":"Nat Biotechnol"},{"key":"2025031010541706500_ref43","doi-asserted-by":"publisher","first-page":"1355","DOI":"10.1038\/s41592-023-01938-4","article-title":"SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks","volume":"20","author":"Bravo Gonz\u00e1lez-Blas","year":"2023","journal-title":"Nat Methods"},{"key":"2025031010541706500_ref44","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s41540-023-00312-6","article-title":"Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data","volume":"9","author":"Kim","year":"2023","journal-title":"NPJ Syst Biol Appl"},{"key":"2025031010541706500_ref45","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1038\/s41576-023-00618-5","article-title":"Gene regulatory network inference in the era of single-cell multi-omics","volume":"24","author":"Badia","year":"2023","journal-title":"Nat Rev Genet"},{"key":"2025031010541706500_ref46","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","article-title":"Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data","volume":"17","author":"Pratapa","year":"2020","journal-title":"Nat Methods"},{"key":"2025031010541706500_ref47","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.617282","article-title":"Evaluating the reproducibility of single-cell gene regulatory network inference algorithms","volume":"12","author":"Kang","year":"2021","journal-title":"Front Genet"},{"key":"2025031010541706500_ref48","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","article-title":"WGCNA: an R package for weighted correlation network analysis","volume":"9","author":"Langfelder","year":"2008","journal-title":"BMC Bioinformatics"},{"key":"2025031010541706500_ref49","doi-asserted-by":"publisher","first-page":"424","DOI":"10.2307\/1912791","article-title":"Investigating causal relations by econometric models and cross-spectral methods","volume":"37","author":"Granger","year":"1969","journal-title":"Econometrica"},{"key":"2025031010541706500_ref50","doi-asserted-by":"crossref","first-page":"8562","DOI":"10.1609\/aaai.v37i7.26031","article-title":"Causal recurrent Variational autoencoder for medical time series generation","volume":"37","author":"Li","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2025031010541706500_ref51","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad335","article-title":"Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations","volume":"24","author":"Lei","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025031010541706500_ref52","volume-title":"Proceedings of Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation","author":"Cho","year":"2014:103\u2013111"},{"key":"2025031010541706500_ref53","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"SCANPY: Large-scale single-cell gene expression data analysis","volume":"19","author":"Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2025031010541706500_ref54","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s13059-019-1663-x","article-title":"PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells","volume":"20","author":"Wolf","year":"2019","journal-title":"Genome Biol"},{"key":"2025031010541706500_ref55","doi-asserted-by":"crossref","first-page":"682","DOI":"10.3390\/math11030682","article-title":"Recent advances in stochastic gradient descent in deep learning","volume":"11","author":"Tian","year":"2023","journal-title":"Mathematics"},{"key":"2025031010541706500_ref56","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1002\/cpa.20042","article-title":"An iterative thresholding algorithm for linear inverse problems with a sparsity constraint","volume":"57","author":"Daubechies","year":"2004","journal-title":"Communications on Pure and Applied Mathematics"},{"key":"2025031010541706500_ref57","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1186\/s12864-018-4772-0","article-title":"Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics","volume":"19","author":"Street","year":"2018","journal-title":"BMC Genomics"},{"key":"2025031010541706500_ref58","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41586-023-06812-z","article-title":"A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain","volume":"624","author":"Yao","year":"2023","journal-title":"Nature"},{"key":"2025031010541706500_ref59","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41596-019-0128-8","article-title":"Protocol update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0)","volume":"14","author":"Mi","year":"2019","journal-title":"Nat Protoc"},{"key":"2025031010541706500_ref60","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-1-60761-175-2_7","article-title":"PANTHER pathway: an ontology-based pathway database coupled with data analysis tools","volume":"563","author":"Mi","year":"2009","journal-title":"Methods Mol Biol"},{"key":"2025031010541706500_ref61","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/pro.4218","article-title":"PANTHER: making genome-scale phylogenetics accessible to all","volume":"31","author":"Thomas","year":"2022","journal-title":"Protein Sci"},{"key":"2025031010541706500_ref62","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1186\/s12859-018-2217-z","article-title":"Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data","volume":"19","author":"Chen","journal-title":"BMC Bioinformatics"},{"key":"2025031010541706500_ref63","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"CoRR"},{"key":"2025031010541706500_ref64","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1145\/1143844.1143874","volume-title":"Proceedings of the 23rd international conference on Machine learning","author":"Davis","year":"2006"},{"key":"2025031010541706500_ref65","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202308934","article-title":"Deep batch integration and denoise of single-cell RNA-seq data","volume":"11","author":"Qin","year":"2024","journal-title":"Adv Sci (Weinh)"},{"key":"2025031010541706500_ref66","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1093\/bioinformatics\/bty1044","article-title":"M3Drop: dropout-based feature selection for scRNASeq","volume":"35","author":"Andrews","year":"2019","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref67","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1093\/bioinformatics\/btz726","article-title":"bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data","volume":"36","author":"Tang","year":"2020","journal-title":"Bioinformatics"},{"key":"2025031010541706500_ref68","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41587-019-0379-5","article-title":"Droplet scRNA-seq is not zero-inflated","volume":"38","author":"Svensson","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2025031010541706500_ref69","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s13059-018-1438-9","article-title":"UMI-count modeling and differential expression analysis for single-cell RNA sequencing","volume":"19","author":"Chen","year":"2018","journal-title":"Genome Biol"},{"key":"2025031010541706500_ref70","doi-asserted-by":"publisher","DOI":"10.1101\/079509","volume-title":"SCORPIUS Improves Trajectory Inference and Identifies Novel Modules in Dendritic Cell Development","author":"Cannoodt","year":"2016"},{"key":"2025031010541706500_ref71","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"Saelens","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2025031010541706500_ref72","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1002\/glia.20933","article-title":"Cell-context specific role of the E2F\/Rb pathway in development and disease","volume":"58","author":"Swiss","year":"2010","journal-title":"Glia"},{"key":"2025031010541706500_ref73","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1101\/gad.186601","article-title":"The transcription factor Sox10 is a key regulator of peripheral glial development","volume":"15","author":"Britsch","year":"2001","journal-title":"Genes Dev"},{"key":"2025031010541706500_ref74","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12060687","article-title":"Immediate early gene c-fos in the brain: focus on glial cells","volume":"12","author":"Cruz-Mendoza","year":"2022","journal-title":"Brain Sci"},{"key":"2025031010541706500_ref75","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.cell.2021.12.003","article-title":"BDNF signaling in context: from synaptic regulation to psychiatric disorders","volume":"185","author":"Wang","year":"2022","journal-title":"Cell"},{"key":"2025031010541706500_ref76","doi-asserted-by":"publisher","DOI":"10.3390\/jpm13040652","article-title":"The role of BDNF as a biomarker in cognitive and sensory neurodegeneration","volume":"13","author":"Pisani","year":"2023","journal-title":"J Pers Med"},{"key":"2025031010541706500_ref77","doi-asserted-by":"publisher","first-page":"10931","DOI":"10.1093\/cercor\/bhad340","article-title":"Regulation of young-adult neurogenesis and neuronal differentiation by neural cell adhesion molecule 2 (NCAM2)","volume":"33","author":"Ortega-Gasco","year":"2023","journal-title":"Cereb Cortex"},{"key":"2025031010541706500_ref78","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1152\/physrev.00005.2009","article-title":"Reward processing by the opioid system in the brain","volume":"89","author":"Le Merrer","year":"2009","journal-title":"Physiol Rev"},{"key":"2025031010541706500_ref79","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1146\/annurev-statistics-040120-010930","article-title":"GRANGER causality: a review and recent advances","volume":"9","author":"Shojaie","year":"2022","journal-title":"Annu Rev Stat Appl"},{"key":"2025031010541706500_ref80","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf089\/62364447\/bbaf089.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/2\/bbaf089\/62364447\/bbaf089.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T10:54:48Z","timestamp":1741604088000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf089\/8068119"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":80,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,3,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf089","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,3]]},"published":{"date-parts":[[2025,3]]},"article-number":"bbaf089"}}