{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:07:39Z","timestamp":1769825259443,"version":"3.49.0"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T00:00:00Z","timestamp":1661990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201300"],"award-info":[{"award-number":["2021YFF1201300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872216"],"award-info":[{"award-number":["61872216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2125007"],"award-info":[{"award-number":["T2125007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31900862"],"award-info":[{"award-number":["31900862"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Turing AI Institute of Nanjing and the Tsinghua-Toyota Joint Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Computational recovery of gene regulatory network (GRN) has recently undergone a great shift from bulk-cell towards designing algorithms targeting single-cell data. In this work, we investigate whether the widely available bulk-cell data could be leveraged to assist the GRN predictions for single cells. We infer cell-type-specific GRNs from both the single-cell RNA sequencing data and the generic GRN derived from the bulk cells by constructing a weakly supervised learning framework based on the axial transformer. We verify our assumption that the bulk-cell transcriptomic data are a valuable resource, which could improve the prediction of single-cell GRN by conducting extensive experiments. Our GRN-transformer achieves the state-of-the-art prediction accuracy in comparison to existing supervised and unsupervised approaches. In addition, we show that our method can identify important transcription factors and potential regulations for Alzheimer\u2019s disease risk genes by using the predicted GRN. Availability: The implementation of GRN-transformer is available at https:\/\/github.com\/HantaoShu\/GRN-Transformer.<\/jats:p>","DOI":"10.1093\/bib\/bbac389","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T23:59:08Z","timestamp":1662595148000},"source":"Crossref","is-referenced-by-count":19,"title":["Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data"],"prefix":"10.1093","volume":"23","author":[{"given":"Hantao","family":"Shu","sequence":"first","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University , Beijing 100084, China"}]},{"given":"Fan","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University , IN 47907, United States"}]},{"given":"Jingtian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Genomic Analysis Laboratory, The Salk Institute for Biological Studies , La Jolla, CA 92037, United States"},{"name":"Bioinformatics Program, University of California , San Diego, La Jolla, CA 92093, United States"}]},{"given":"Yexiang","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University , IN 47907, United States"}]},{"given":"Dan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University , Beijing 100084, China"}]},{"given":"Jianyang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Institute for Interdisciplinary Information Sciences, Tsinghua University , Beijing 100084, China"}]},{"given":"Jianzhu","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence, Peking University , Beijing 100091, China"}]}],"member":"286","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"issue":"11","key":"2022092013234998300_ref1","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/nmeth.2639","article-title":"Smart-seq2 for sensitive full-length transcriptome profiling in single cells","volume":"10","author":"Picelli","year":"2013","journal-title":"Nat Methods"},{"issue":"5","key":"2022092013234998300_ref2","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"issue":"1","key":"2022092013234998300_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-0938-8","article-title":"Cel-seq2: sensitive highly-multiplexed single-cell RNA-seq","volume":"17","author":"Hashimshony","year":"2016","journal-title":"Genome Biol"},{"issue":"11","key":"2022092013234998300_ref4","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1038\/nbt.3711","article-title":"Revealing the vectors of cellular identity with single-cell genomics","volume":"34","author":"Wagner","year":"2016","journal-title":"Nat Biotechnol"},{"issue":"10","key":"2022092013234998300_ref5","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1038\/nrg.2016.87","article-title":"Network biology concepts in complex disease comorbidities","volume":"17","author":"Jessica Xin","year":"2016","journal-title":"Nat Rev Genet"},{"issue":"7","key":"2022092013234998300_ref6","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1038\/s43588-021-00099-8","article-title":"Modeling gene regulatory networks using neural network architectures","volume":"1","author":"Shu","year":"2021","journal-title":"Nat Comput Sci"},{"issue":"9","key":"2022092013234998300_ref7","doi-asserted-by":"crossref","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"},{"issue":"12","key":"2022092013234998300_ref8","doi-asserted-by":"crossref","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"},{"issue":"3","key":"2022092013234998300_ref9","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 Systems"},{"issue":"15","key":"2022092013234998300_ref10","doi-asserted-by":"crossref","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"},{"issue":"6","key":"2022092013234998300_ref11","first-page":"665","article-title":"PPCOR: an R package for a fast calculation to semi-partial correlation coefficients","volume":"22","author":"Kim","year":"2015","journal-title":"Commun Stat Appl Methods"},{"issue":"2","key":"2022092013234998300_ref12","doi-asserted-by":"crossref","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":"Gao","year":"2018","journal-title":"Bioinformatics"},{"issue":"2","key":"2022092013234998300_ref13","doi-asserted-by":"crossref","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":"2022092013234998300_ref14","doi-asserted-by":"publisher","DOI":"10.1101\/2020.02.17.947416","article-title":"Celloracle: dissecting cell identity via network inference and in silico gene perturbation","author":"Kamimoto","year":"2020"},{"key":"2022092013234998300_ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.xgen.2022.100166","article-title":"Functional inference of gene regulation using single-cell multi-omics","volume-title":"Cell Genomics","author":"Kartha"},{"issue":"52","key":"2022092013234998300_ref16","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"},{"key":"2022092013234998300_ref17","doi-asserted-by":"crossref","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"},{"issue":"1","key":"2022092013234998300_ref18","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1093\/bioinformatics\/btz529","article-title":"Inference of differential gene regulatory networks based on gene expression and genetic perturbation data","volume":"36","author":"Zhou","year":"2020","journal-title":"Bioinformatics"},{"key":"2022092013234998300_ref19","doi-asserted-by":"crossref","first-page":"200","DOI":"10.3389\/fonc.2019.00200","article-title":"An integrated regulatory network based on comprehensive analysis of mRNA expression, gene methylation and expression of long non-coding RNAs (lncRNAs) in myelodysplastic syndromes","volume":"9","author":"Zhao","year":"2019","journal-title":"Front Oncol"},{"key":"2022092013234998300_ref20","doi-asserted-by":"publisher","DOI":"10.1101\/2021.10.31.466658","article-title":"Biological network inference from single-cell multi-omics data using heterogeneous graph transformer","volume-title":"bioRxiv","author":"Ma","year":"2021"},{"key":"2022092013234998300_ref21","doi-asserted-by":"crossref","DOI":"10.1038\/s42256-022-00469-5","article-title":"Inferring transcription factor regulatory networks from single-cell atac-seq data based on graph neural networks","volume":"4","author":"Li","year":"2022","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2022092013234998300_ref22","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13059-019-1681-8","article-title":"Scrabble: single-cell RNA-seq imputation constrained by bulk RNA-seq data","volume":"20","author":"Peng","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"2022092013234998300_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02075-3","article-title":"Single-cell atac-seq signal extraction and enhancement with scate","volume":"21","author":"Ji","year":"2020","journal-title":"Genome Biol"},{"key":"2022092013234998300_ref24","doi-asserted-by":"publisher","DOI":"10.1101\/2021.08.01.454654","article-title":"Deep transfer learning of drug sensitivity by integrating bulk and single-cell RNA-seq data","volume-title":"bioRxiv","author":"Chen","year":"2021"},{"key":"2022092013234998300_ref25","doi-asserted-by":"crossref","DOI":"10.1093\/database\/bav095","article-title":"Regnetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse","volume":"2015","author":"Liu","year":"2015","journal-title":"Database"},{"issue":"D1","key":"2022092013234998300_ref26","doi-asserted-by":"crossref","first-page":"D380","DOI":"10.1093\/nar\/gkx1013","article-title":"Trrust v2: an expanded reference database of human and mouse transcriptional regulatory interactions","volume":"46","author":"Han","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"2022092013234998300_ref27","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1101\/gr.240663.118","article-title":"Benchmark and integration of resources for the estimation of human transcription factor activities","volume":"29","author":"Garcia-Alonso","year":"2019","journal-title":"Genome Res"},{"key":"2022092013234998300_ref28","article-title":"Axial attention in multidimensional transformers","author":"Ho","year":"2019"},{"issue":"7873","key":"2022092013234998300_ref29","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with alphafold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2022092013234998300_ref30","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/P19-1282","article-title":"Is attention interpretable?","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Serrano"},{"key":"2022092013234998300_ref31","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/W19-4808","article-title":"Analyzing the structure of attention in a transformer language model","volume-title":"Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP","author":"Vig"},{"key":"2022092013234998300_ref32","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"issue":"9","key":"2022092013234998300_ref33","doi-asserted-by":"crossref","first-page":"1906","DOI":"10.1016\/j.jmva.2008.01.016","article-title":"Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data","volume":"99","author":"Kubokawa","year":"2008","journal-title":"J Multivariate Anal"},{"key":"2022092013234998300_ref34","article-title":"Layer normalization","volume-title":"arXiv","author":"Ba"},{"key":"2022092013234998300_ref35","article-title":"Efficient attention: attention with linear complexities","author":"Shen","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"},{"key":"2022092013234998300_ref36","article-title":"Rethinking attention with performers","volume-title":"International Conference on Learning Representations","author":"Choromanski"},{"key":"2022092013234998300_ref37","volume-title":"International Conference on Learning Representations","author":"Kitaev","year":"2020"},{"key":"2022092013234998300_ref38","article-title":"Decoupled weight decay regularization","volume-title":"International Conference on Learning Representations","author":"Loshchilov"},{"issue":"3","key":"2022092013234998300_ref39","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1093\/bioinformatics\/btm604","article-title":"Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction","volume":"24","author":"Dunn","year":"2008","journal-title":"Bioinformatics"},{"key":"2022092013234998300_ref40","doi-asserted-by":"crossref","DOI":"10.1101\/2021.02.12.430858","article-title":"MSA transformer","volume-title":"International Conference on Machine Learning","author":"Rao"},{"key":"2022092013234998300_ref41","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"issue":"3","key":"2022092013234998300_ref42","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.cels.2020.08.003","article-title":"Sergio: a single-cell expression simulator guided by gene regulatory networks","volume":"11","author":"Dibaeinia","year":"2020","journal-title":"Cell Syst"},{"issue":"1","key":"2022092013234998300_ref43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-02866-0","article-title":"Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs","volume":"9","author":"Hayashi","year":"2018","journal-title":"Nat Commun"},{"issue":"7505","key":"2022092013234998300_ref44","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/nature13437","article-title":"Single-cell RNA-seq reveals dynamic paracrine control of cellular variation","volume":"510","author":"Shalek","year":"2014","journal-title":"Nature"},{"issue":"8","key":"2022092013234998300_ref45","first-page":"e20","article-title":"A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation","volume":"128","author":"Nestorowa","year":"2016","journal-title":"Blood, J Am Soc Hematol"},{"issue":"7659","key":"2022092013234998300_ref46","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/nature22796","article-title":"Multilineage communication regulates human liver bud development from pluripotency","volume":"546","author":"Camp","year":"2017","journal-title":"Nature"},{"issue":"1","key":"2022092013234998300_ref47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-016-1033-x","article-title":"Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm","volume":"17","author":"Chu","year":"2016","journal-title":"Genome Biol"},{"issue":"D1","key":"2022092013234998300_ref48","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"String v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"2022092013234998300_ref49","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1038\/s41593-019-0539-4","article-title":"A single-cell atlas of entorhinal cortex from individuals with Alzheimer\u2019s disease reveals cell-type-specific gene expression regulation","volume":"22","author":"Grubman","year":"2019","journal-title":"Nat Neurosci"},{"issue":"D1","key":"2022092013234998300_ref50","first-page":"D87","article-title":"Jaspar 2020: update of the open-access database of transcription factor binding profiles","volume":"48","author":"Fornes","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2022092013234998300_ref51","first-page":"1","article-title":"Scanpy: large-scale single-cell gene expression data analysis","volume":"19","author":"Alexander Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2022092013234998300_ref52","volume-title":"Publicationes Mathematicae","author":"Erdos"},{"issue":"21","key":"2022092013234998300_ref53","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1103\/PhysRevLett.85.4633","article-title":"Structure of growing networks with preferential linking","volume":"85","author":"Dorogovtsev","year":"2000","journal-title":"Phys Rev Lett"},{"issue":"1","key":"2022092013234998300_ref54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"issue":"Suppl 2","key":"2022092013234998300_ref55","doi-asserted-by":"crossref","first-page":"S72","DOI":"10.5213\/inj.1938196.098","article-title":"Methyl-CPG binding protein 2 in Alzheimer dementia","volume":"23","author":"Kim","year":"2019","journal-title":"Int Neurourol J"},{"key":"2022092013234998300_ref56","first-page":"276","article-title":"Exploring the key genes and identification of potential diagnosis biomarkers in Alzheimer\u2019s disease using bioinformatics analysis","volume":"13","author":"Wuhan","year":"2021","journal-title":"Front Aging Neurosci"},{"issue":"4","key":"2022092013234998300_ref57","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.nurt.2010.05.017","article-title":"Astrocytes in Alzheimer\u2019s disease","volume":"7","author":"Verkhratsky","year":"2010","journal-title":"Neurotherapeutics"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac389\/45937411\/bbac389.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac389\/45937411\/bbac389.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T18:13:41Z","timestamp":1663697621000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac389\/6693602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac389","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9]]},"published":{"date-parts":[[2022,9]]},"article-number":"bbac389"}}