{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:23:27Z","timestamp":1775607807383,"version":"3.50.1"},"reference-count":87,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772362"],"award-info":[{"award-number":["61772362"]}],"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":["61902271"],"award-info":[{"award-number":["61902271"]}],"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":["61972280"],"award-info":[{"award-number":["61972280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0908401"],"award-info":[{"award-number":["2020YFA0908401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0908402"],"award-info":[{"award-number":["2020YFA0908402"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0908400"],"award-info":[{"award-number":["2020YFA0908400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0910405"],"award-info":[{"award-number":["2018YFC0910405"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC0908400"],"award-info":[{"award-number":["2017YFC0908400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.<\/jats:p>","DOI":"10.1093\/bib\/bbab009","type":"journal-article","created":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T13:18:14Z","timestamp":1610111894000},"source":"Crossref","is-referenced-by-count":99,"title":["A comprehensive overview and critical evaluation of gene regulatory network inference technologies"],"prefix":"10.1093","volume":"22","author":[{"given":"Mengyuan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}]},{"given":"Wenying","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[{"name":"University of South Carolina, Tianjin, China"}]},{"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China"}]}],"member":"286","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"issue":"2","key":"2021090813522473900_ref1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ydbio.2009.10.032","article-title":"Challenges for modeling global gene regulatory networks during development: insights from Drosophila","volume":"340","author":"Wilczynski","year":"2010","journal-title":"Dev Biol"},{"key":"2021090813522473900_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4939-8882-2_1","article-title":"Gene regulatory network inference: an introductory survey","volume":"1883","author":"Huynh-Thu","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2021090813522473900_ref3","doi-asserted-by":"crossref","first-page":"457","DOI":"10.3389\/fgene.2020.00457","article-title":"Gene regulatory network inference: connecting plant biology and mathematical modeling","volume":"11","author":"Broeck","year":"2020","journal-title":"Front Genet"},{"issue":"4","key":"2021090813522473900_ref4","first-page":"712","article-title":"Dynamic and modular gene regulatory networks drive the development of gametogenesis","volume":"18","author":"Che","year":"2016","journal-title":"Brief Bioinform"},{"issue":"4","key":"2021090813522473900_ref5","first-page":"1","article-title":"Critical microRNAs and regulatory motifs in cleft palate identified by a conserved miRNA\u2013TF\u2013gene network approach in humans and mice","volume":"21","author":"Li","year":"2019","journal-title":"Brief Bioinform"},{"issue":"2","key":"2021090813522473900_ref6","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1093\/bib\/bbt090","article-title":"Next-generation bioinformatics: connecting bases to genes, networks and disease","volume":"15","author":"Horton","year":"2014","journal-title":"Brief Bioinform"},{"issue":"1","key":"2021090813522473900_ref7","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/s13059-019-1713-4","article-title":"Single-cell transcriptomics unveils gene regulatory network plasticity","volume":"20","author":"Iacono","year":"2019","journal-title":"Genome Biol"},{"issue":"3","key":"2021090813522473900_ref8","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1093\/bib\/bbx151","article-title":"Systems bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches","volume":"20","author":"Oulas","year":"2017","journal-title":"Brief Bioinform"},{"key":"2021090813522473900_ref9","first-page":"38","article-title":"Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks","volume":"2","author":"Emmertstreib","year":"2014","journal-title":"Front Cell Dev Biol"},{"issue":"7","key":"2021090813522473900_ref10","doi-asserted-by":"crossref","first-page":"831","DOI":"10.3390\/genes11070831","article-title":"Computational analysis of the global effects of Ly6E in the immune response to coronavirus infection using gene networks","volume":"11","author":"Delgado-Chaves","year":"2020","journal-title":"Genes"},{"issue":"5","key":"2021090813522473900_ref11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gm340","article-title":"Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets","volume":"4","author":"Madhamshettiwar","year":"2012","journal-title":"Genome Med"},{"issue":"5594","key":"2021090813522473900_ref12","first-page":"799","article-title":"Transcriptional regulatory networks in Saccharomyces cerevisiae","volume":"298","author":"Tong","year":"2012","journal-title":"Science"},{"key":"2021090813522473900_ref13","first-page":"1","article-title":"Biological networks for cancer candidate biomarkers discovery","volume":"15","author":"Yan","year":"2016","journal-title":"Cancer Inform"},{"issue":"6","key":"2021090813522473900_ref14","doi-asserted-by":"crossref","first-page":"194430","DOI":"10.1016\/j.bbagrm.2019.194430","article-title":"Gene regulatory network inference resources: a practical overview","volume":"1863","author":"Mercatelli","year":"2020","journal-title":"Biochim Biophys Acta Gene Regul Mech"},{"key":"2021090813522473900_ref15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1038\/4462","article-title":"Exploring the new world of the genome with DNA microarrays","volume":"21","author":"Brown","year":"1999","journal-title":"Nat Genet"},{"issue":"5235","key":"2021090813522473900_ref16","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1126\/science.270.5235.467","article-title":"Quantitative monitoring of gene expression patterns with a complementary DNA microarray","volume":"270","author":"Schena","year":"1995","journal-title":"Science"},{"issue":"10","key":"2021090813522473900_ref17","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1016\/j.bbadis.2014.06.015","article-title":"Next generation sequencing technology: advances and applications","volume":"1842","author":"Buermans","year":"2014","journal-title":"Biochim Biophys Acta"},{"issue":"1","key":"2021090813522473900_ref18","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nrg2484","article-title":"RNA-seq: a revolutionary tool for transcriptomics","volume":"10","author":"Wang","year":"2010","journal-title":"Nat Rev Genet"},{"issue":"10","key":"2021090813522473900_ref19","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1038\/nrg2641","article-title":"ChIP-seq: advantages and challenges of a maturing technology","volume":"10","author":"Park","year":"2009","journal-title":"Nat Rev Genet"},{"issue":"Database issue","key":"2021090813522473900_ref20","first-page":"D991","article-title":"NCBI GEO: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"SI","key":"2021090813522473900_ref21","first-page":"D553","article-title":"ArrayExpress\u2014a public repository for microarray gene expression data at the EBI","volume":"33","author":"Brazma","year":"2005","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813522473900_ref22","doi-asserted-by":"crossref","first-page":"D133","DOI":"10.1093\/nar\/gkv1156","article-title":"RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond","volume":"44","author":"Gama-Castro","year":"2015","journal-title":"Nucleic Acids Res"},{"issue":"Database issue","key":"2021090813522473900_ref23","first-page":"1049","article-title":"Gene Ontology Consortium: going forward","volume":"43","author":"Blake","year":"2015","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"2021090813522473900_ref24","first-page":"D457","article-title":"KEGG as a reference resource for gene and protein annotation","volume":"44","author":"Minoru","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2021090813522473900_ref25","first-page":"636","article-title":"The ENCODE (encyclopedia of DNA elements) project","volume-title":"Science","author":"Feingold","year":"2004"},{"issue":"4","key":"2021090813522473900_ref26","first-page":"408","article-title":"Computational methods for discovering gene networks from expression data","volume":"10","author":"Lee","year":"2009","journal-title":"Brief Bioinform"},{"issue":"2","key":"2021090813522473900_ref27","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1093\/bib\/bbt034","article-title":"Supervised, semi-supervised and unsupervised inference of gene regulatory networks","volume":"15","author":"Maetschke","year":"2014","journal-title":"Brief Bioinform"},{"issue":"3","key":"2021090813522473900_ref28","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1093\/bib\/bbq078","article-title":"Validation of gene regulatory networks: scientific and inferential","volume":"12","author":"Dougherty","year":"2011","journal-title":"Brief Bioinform"},{"issue":"18","key":"2021090813522473900_ref29","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1093\/bioinformatics\/btz105","article-title":"Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants","volume":"35","author":"Muldoon","year":"2019","journal-title":"Bioinformatics"},{"issue":"91","key":"2021090813522473900_ref30","doi-asserted-by":"crossref","first-page":"20130505","DOI":"10.1098\/rsif.2013.0505","article-title":"Reverse engineering and identification in systems biology: strategies, perspectives and challenges","volume":"11","author":"Villaverde","year":"2014","journal-title":"J R Soc Interface"},{"key":"2021090813522473900_ref31","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.copbio.2019.12.002","article-title":"Network inference in systems biology: recent developments, challenges, and applications","volume":"63","author":"Saint-Antoine","year":"2020","journal-title":"Curr Opin Biotechnol"},{"key":"2021090813522473900_ref32","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.compbiomed.2014.02.011","article-title":"A review on the computational approaches for gene regulatory network construction","volume":"48","author":"Chai","year":"2014","journal-title":"Comput Biol Med"},{"issue":"6","key":"2021090813522473900_ref33","first-page":"1","article-title":"Current approaches to gene regulatory network modelling","volume":"8","author":"Schlitt","year":"2007","journal-title":"BMC Bioinform"},{"issue":"8","key":"2021090813522473900_ref34","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","article-title":"Wisdom of crowds for robust gene network inference","volume":"9","author":"Marbach","year":"2012","journal-title":"Nat Methods"},{"issue":"3","key":"2021090813522473900_ref35","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/0022-5193(73)90247-6","article-title":"Boolean formalization of genetic control circuits","volume":"42","author":"Thomas","year":"1973","journal-title":"J Theor Biol"},{"issue":"6","key":"2021090813522473900_ref36","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TSP.2006.873740","article-title":"Optimal infinite-horizon control for probabilistic boolean networks","volume":"54","author":"Pal","year":"2006","journal-title":"IEEE Trans Signal Process"},{"issue":"7","key":"2021090813522473900_ref37","doi-asserted-by":"crossref","first-page":"511","DOI":"10.2174\/138920209789208237","article-title":"A tutorial on analysis and simulation of boolean gene regulatory network models","volume":"10","author":"Xiao","year":"2009","journal-title":"Curr Genomics"},{"issue":"Suppl_2","key":"2021090813522473900_ref38","doi-asserted-by":"crossref","first-page":"ii138","DOI":"10.1093\/bioinformatics\/btg1071","article-title":"Gene networks inference using dynamic Bayesian networks","volume":"19","author":"Bruno-Edouard","year":"2003","journal-title":"Bioinformatics"},{"issue":"3","key":"2021090813522473900_ref39","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1093\/bib\/4.3.228","article-title":"Inferring gene networks from time series microarray data using dynamic Bayesian networks","volume":"4","author":"Kim","year":"2003","journal-title":"Brief Bioinform"},{"issue":"6","key":"2021090813522473900_ref40","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":"Sanchezcastillo","year":"2018","journal-title":"Bioinformatics"},{"issue":"8","key":"2021090813522473900_ref41","doi-asserted-by":"crossref","first-page":"e1005024","DOI":"10.1371\/journal.pcbi.1005024","article-title":"Inference of gene regulatory network based on local Bayesian networks","volume":"12","author":"Liu","year":"2016","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2021090813522473900_ref42","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/1752-0509-6-145","article-title":"TIGRESS: trustful inference of gene regulation using stability selection","volume":"6","author":"Haury","year":"2012","journal-title":"BMC Syst Biol"},{"issue":"15","key":"2021090813522473900_ref43","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":"2021090813522473900_ref44","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1109\/TCBB.2018.2825446","article-title":"Inferring large-scale gene regulatory networks using a randomized algorithm based on singular value decomposition","volume":"16","author":"Fan","year":"2018","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"19","key":"2021090813522473900_ref45","doi-asserted-by":"crossref","first-page":"4885","DOI":"10.1093\/bioinformatics\/btaa032","article-title":"Inference of gene regulatory networks based on nonlinear ordinary differential equations","volume":"36","author":"Ma","year":"2020","journal-title":"Bioinformatics"},{"issue":"12","key":"2021090813522473900_ref46","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1093\/bioinformatics\/btaa267","article-title":"GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem","volume":"36","author":"Tsai","year":"2020","journal-title":"Bioinformatics"},{"issue":"39","key":"2021090813522473900_ref47","doi-asserted-by":"crossref","first-page":"36168","DOI":"10.1074\/jbc.M104391200","article-title":"Neural model of the genetic network","volume":"276","author":"Vohradsky","year":"2001","journal-title":"J Biol Chem"},{"key":"2021090813522473900_ref48","first-page":"1","volume-title":"IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology","author":"Ressom","year":"2006"},{"issue":"9","key":"2021090813522473900_ref49","doi-asserted-by":"crossref","first-page":"e1007324","DOI":"10.1371\/journal.pcbi.1007324","article-title":"Predicting gene regulatory interactions based on spatial gene expression data and deep learning","volume":"15","author":"Yang","year":"2019","journal-title":"PLoS Comput Biol"},{"issue":"52","key":"2021090813522473900_ref50","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"},{"issue":"1","key":"2021090813522473900_ref51","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1186\/1471-2105-13-328","article-title":"Comparison of co-expression measures: mutual information, correlation, and model based indices","volume":"13","author":"Song","year":"2012","journal-title":"BMC Bioinform"},{"issue":"1","key":"2021090813522473900_ref52","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1093\/bioinformatics\/bts619","article-title":"NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference","volume":"29","author":"Zhang","year":"2013","journal-title":"Bioinformatics"},{"issue":"4","key":"2021090813522473900_ref53","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1038\/ng1532","article-title":"Reverse engineering of regulatory networks in human B cells","volume":"37","author":"Basso","year":"2005","journal-title":"Nat Genet"},{"issue":"1","key":"2021090813522473900_ref54","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1371\/journal.pbio.0050008","article-title":"Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles","volume":"5","author":"Faith","year":"2007","journal-title":"PLoS Biol"},{"issue":"5643","key":"2021090813522473900_ref55","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1126\/science.1087447","article-title":"A gene-coexpression network for global discovery of conserved genetic modules","volume":"302","author":"Joshua","year":"2003","journal-title":"Science"},{"issue":"S7","key":"2021090813522473900_ref56","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s12918-018-0635-1","article-title":"MICRAT: a novel algorithm for inferring gene regulatory networks using time series gene expression data","volume":"12","author":"Yang","year":"2018","journal-title":"BMC Syst Biol"},{"issue":"18","key":"2021090813522473900_ref57","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1073\/pnas.1522586113","article-title":"Part mutual information for quantifying direct associations in networks","volume":"113","author":"Zhao","year":"2016","journal-title":"Proc Natl Acad Sci USA"},{"key":"2021090813522473900_ref58","doi-asserted-by":"crossref","first-page":"e31","DOI":"10.1093\/nar\/gku1315","article-title":"Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks","volume":"43","author":"Zhang","year":"2015","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"2021090813522473900_ref59","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"issue":"2","key":"2021090813522473900_ref60","first-page":"325","article-title":"A review on machine learning principles for multi-view biological data integration","volume":"19","author":"Li","year":"2016","journal-title":"Brief Bioinform"},{"issue":"8","key":"2021090813522473900_ref61","doi-asserted-by":"crossref","first-page":"2522","DOI":"10.1093\/bioinformatics\/btz950","article-title":"LiPLike: towards gene regulatory network predictions of high certainty","volume":"36","author":"Magnusson","year":"2020","journal-title":"Bioinformatics"},{"issue":"1","key":"2021090813522473900_ref62","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"},{"issue":"9","key":"2021090813522473900_ref63","doi-asserted-by":"crossref","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"},{"issue":"1","key":"2021090813522473900_ref64","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1186\/s12859-020-03639-7","article-title":"PFBNet: a priori-fused boosting method for gene regulatory network inference","volume":"21","author":"Che","year":"2020","journal-title":"BMC Bioinform"},{"issue":"11","key":"2021090813522473900_ref65","doi-asserted-by":"crossref","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"},{"issue":"12","key":"2021090813522473900_ref66","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":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"2021090813522473900_ref67","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/s41540-020-0140-1","article-title":"Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data","volume":"6","author":"Razaghi-Moghadam","year":"2020","journal-title":"NPJ Syst Biol Appl"},{"issue":"5","key":"2021090813522473900_ref68","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1109\/JBHI.2019.2931997","article-title":"Inferring gene regulatory networks of metabolic enzymes using gradient boosted trees","volume":"24","author":"Zhang","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"issue":"11","key":"2021090813522473900_ref69","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1093\/bioinformatics\/bty908","article-title":"BiXGBoost: a scalable, flexible boosting based method for reconstructing gene regulatory networks","volume":"35","author":"Zheng","year":"2019","journal-title":"Bioinformatics"},{"issue":"10","key":"2021090813522473900_ref70","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1093\/bioinformatics\/btu863","article-title":"Combining tree-based and dynamical systems for the inference of gene regulatory networks","volume":"31","author":"Huynh-Thu","year":"2015","journal-title":"Bioinformatics"},{"issue":"16","key":"2021090813522473900_ref71","doi-asserted-by":"crossref","first-page":"I76","DOI":"10.1093\/bioinformatics\/btn273","article-title":"SIRENE: supervised inference of regulatory networks","volume":"24","author":"Mordelet","year":"2008","journal-title":"Bioinformatics"},{"issue":"14","key":"2021090813522473900_ref72","doi-asserted-by":"crossref","first-page":"6286","DOI":"10.1073\/pnas.0913357107","article-title":"Revealing strengths and weaknesses of methods for gene network inference","volume":"107","author":"Marbach","year":"2010","journal-title":"Proc Natl Acad Sci USA"},{"issue":"16","key":"2021090813522473900_ref73","first-page":"229","article-title":"Generating realistic in silico gene networks for performance assessment of reverse engineering methods","volume":"2","author":"Daniel","year":"2009","journal-title":"J Comput Biol"},{"key":"2021090813522473900_ref74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1196\/annals.1407.021","article-title":"Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference","volume":"1115","author":"Stolovitzky","year":"2008","journal-title":"Ann N Y Acad Sci"},{"key":"2021090813522473900_ref75","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/j.1749-6632.2009.04497.x","article-title":"Lessons from the DREAM2 challenges","volume":"1158","author":"Stolovitzky","year":"2009","journal-title":"Ann N Y Acad Sci"},{"issue":"16","key":"2021090813522473900_ref76","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1093\/bioinformatics\/btr373","article-title":"GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods","volume":"27","author":"Schaffter","year":"2011","journal-title":"Bioinformatics"},{"issue":"16","key":"2021090813522473900_ref77","doi-asserted-by":"crossref","first-page":"10555","DOI":"10.1073\/pnas.152046799","article-title":"Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics","volume":"99","author":"Ronen","year":"2002","journal-title":"Proc Natl Acad Sci USA"},{"issue":"1","key":"2021090813522473900_ref78","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/ng881","article-title":"Network motifs in the transcriptional regulation network of Escherichia coli","volume":"31","author":"Shen-Orr","year":"2002","journal-title":"Nat Genet"},{"issue":"1","key":"2021090813522473900_ref79","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1038\/msb.2010.18","article-title":"Metabolomic and transcriptomic stress response of Escherichia coli","volume":"6","author":"Jozefczuk","year":"2010","journal-title":"Mol Syst Biol"},{"key":"2021090813522473900_ref80","article-title":"A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models","author":"Bilmes","year":"1998"},{"issue":"2","key":"2021090813522473900_ref81","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann Statist"},{"issue":"4","key":"2021090813522473900_ref82","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","article-title":"Stability selection","volume":"72","author":"Meinshausen","year":"2010","journal-title":"J R Statist Soc"},{"issue":"7","key":"2021090813522473900_ref83","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1109\/TSP.2008.919638","article-title":"Inference of noisy nonlinear differential equation models for gene regulatory networks using genetic programming and Kalman filtering","volume":"56","author":"Qian","year":"2008","journal-title":"IEEE Trans Signal Process"},{"key":"2021090813522473900_ref84","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1093\/bioinformatics\/btr626","article-title":"Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information","volume":"28","author":"Zhang","year":"2012","journal-title":"Bioinformatics"},{"issue":"7","key":"2021090813522473900_ref85","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1093\/bioinformatics\/btt069","article-title":"Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics","volume":"29","author":"Andrea","year":"2013","journal-title":"Bioinformatics"},{"issue":"6995","key":"2021090813522473900_ref86","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1038\/nature02555","article-title":"Evidence for dynamically organized modularity in the yeast protein\u2013protein interaction network","volume":"430","author":"Han","year":"2004","journal-title":"Nature"},{"issue":"6804","key":"2021090813522473900_ref87","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1038\/35036627","article-title":"The large-scale organization of metabolic networks","volume":"407","author":"Jeong","year":"2000","journal-title":"Nature"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab009\/40260990\/bbab009.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/5\/bbab009\/40260990\/bbab009.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T06:43:54Z","timestamp":1697525034000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab009\/6128842"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,5]]},"references-count":87,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,9,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab009","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,9]]},"published":{"date-parts":[[2021,2,5]]},"article-number":"bbab009"}}