{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T01:57:05Z","timestamp":1771034225302,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T00:00:00Z","timestamp":1482883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T00:00:00Z","timestamp":1482883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61274133"],"award-info":[{"award-number":["No. 61274133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with <jats:italic>p<\/jats:italic> genes into <jats:italic>p<\/jats:italic> subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm. Then, a statistical technique is used to refine the predictions in our method. The proposed method was evaluated on the DREAM4 and DREAM5 benchmark datasets and achieved higher accuracy than the winners of those competitions and other state-of-the-art GRN inference methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Superior accuracy achieved on different benchmark datasets, including both <jats:italic>in silico<\/jats:italic> and <jats:italic>in vivo<\/jats:italic> networks, shows that PLSNET reaches state-of-the-art performance.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-016-1398-6","type":"journal-article","created":{"date-parts":[[2016,12,28]],"date-time":"2016-12-28T10:43:56Z","timestamp":1482921836000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Gene regulatory network inference using PLS-based methods"],"prefix":"10.1186","volume":"17","author":[{"given":"Shun","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,12,28]]},"reference":[{"key":"1398_CR1","doi-asserted-by":"publisher","DOI":"10.1142\/p567","volume-title":"Computational modeling of gene regulatory networks: a primer","author":"H Bolouri","year":"2008","unstructured":"Bolouri H. Computational modeling of gene regulatory networks: a primer. London: Imperial College Press; 2008."},{"issue":"1","key":"1398_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.plrev.2005.01.001","volume":"2","author":"TS Gardner","year":"2005","unstructured":"Gardner TS, Faith JJ. Reverse-engineering transcription control networks. Phys Life Rev. 2005;2(1):65\u201388.","journal-title":"Phys Life Rev"},{"issue":"1","key":"1398_CR3","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/msb4100120","volume":"3","author":"M Bansal","year":"2007","unstructured":"Bansal M, Belcastro V, Ambesi\u2010Impiombato A, et al. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3(1):78.","journal-title":"Mol Syst Biol"},{"issue":"Suppl 6","key":"1398_CR4","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1186\/1471-2105-8-S6-S5","volume":"8","author":"F Markowetz","year":"2007","unstructured":"Markowetz F, Spang R. Inferring cellular networks\u2013a review. BMC Bioinf. 2007;8 Suppl 6:S5.","journal-title":"BMC Bioinf."},{"issue":"4","key":"1398_CR5","first-page":"408","volume":"10","author":"WP Lee","year":"2009","unstructured":"Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Brief Bioinform. 2009;10(4):408\u201323.","journal-title":"Brief Bioinform"},{"issue":"25","key":"1398_CR6","doi-asserted-by":"publisher","first-page":"14863","DOI":"10.1073\/pnas.95.25.14863","volume":"95","author":"MB Eisen","year":"1998","unstructured":"Eisen MB, Spellman PT, Brown PO, et al. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998;95(25):14863\u20138.","journal-title":"Proc Natl Acad Sci"},{"key":"1398_CR7","first-page":"418","volume":"5","author":"AJ Butte","year":"2000","unstructured":"Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000;5:418\u201329.","journal-title":"Pac Symp Biocomput"},{"issue":"1","key":"1398_CR8","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1371\/journal.pbio.0050008","volume":"5","author":"JJ Faith","year":"2007","unstructured":"Faith JJ, Hayete B, Thaden JT, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5(1):e8.","journal-title":"PLoS Biol"},{"issue":"2","key":"1398_CR9","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/nprot.2006.106","volume":"1","author":"AA Margolin","year":"2006","unstructured":"Margolin AA, Wang K, Lim WK, et al. Reverse engineering cellular networks. Nat Protoc. 2006;1(2):662\u201371.","journal-title":"Nat Protoc"},{"issue":"1","key":"1398_CR10","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1186\/1752-0509-4-132","volume":"4","author":"G Altay","year":"2010","unstructured":"Altay G, Emmert-Streib F. Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol. 2010;4(1):132.","journal-title":"BMC Syst Biol"},{"issue":"3","key":"1398_CR11","doi-asserted-by":"publisher","first-page":"e33624","DOI":"10.1371\/journal.pone.0032690","volume":"7","author":"SR de Matos","year":"2012","unstructured":"de Matos SR, Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLoS One. 2012;7(3):e33624.","journal-title":"PLoS One"},{"issue":"10","key":"1398_CR12","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1093\/bioinformatics\/bts143","volume":"28","author":"R K\u00fcffner","year":"2012","unstructured":"K\u00fcffner R, Petri T, Tavakkolkhah P, et al. Inferring gene regulatory networks by ANOVA. Bioinformatics. 2012;28(10):1376\u201382.","journal-title":"Bioinformatics"},{"issue":"5659","key":"1398_CR13","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1126\/science.1094068","volume":"303","author":"N Friedman","year":"2004","unstructured":"Friedman N. Inferring cellular networks using probabilistic graphical models. Science. 2004;303(5659):799\u2013805.","journal-title":"Science"},{"issue":"3-4","key":"1398_CR14","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1089\/106652700750050961","volume":"7","author":"N Friedman","year":"2000","unstructured":"Friedman N, Linial M, Nachman I, et al. Using Bayesian networks to analyze expression data. J Comput Biol. 2000;7(3-4):601\u201320.","journal-title":"J Comput Biol"},{"issue":"1","key":"1398_CR15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/1471-2105-9-91","volume":"9","author":"C Auliac","year":"2008","unstructured":"Auliac C, Frouin V, Gidrol X, et al. Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. BMC Bioinf. 2008;9(1):91.","journal-title":"BMC Bioinf."},{"issue":"18","key":"1398_CR16","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1093\/bioinformatics\/bth448","volume":"20","author":"J Yu","year":"2004","unstructured":"Yu J, Smith VA, Wang P, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics. 2004;20(18):3594\u2013603.","journal-title":"Bioinformatics"},{"issue":"suppl 2","key":"1398_CR17","doi-asserted-by":"publisher","first-page":"ii138","DOI":"10.1093\/bioinformatics\/btg1071","volume":"19","author":"BE Perrin","year":"2003","unstructured":"Perrin BE, Ralaivola L, Mazurie A, et al. Gene networks inference using dynamic Bayesian networks. Bioinformatics. 2003;19 suppl 2:ii138\u201348.","journal-title":"Bioinformatics"},{"issue":"9","key":"1398_CR18","doi-asserted-by":"publisher","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","volume":"5","author":"A Irrthum","year":"2010","unstructured":"Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010;5(9):e12776.","journal-title":"PLoS One"},{"issue":"14","key":"1398_CR19","doi-asserted-by":"publisher","first-page":"6286","DOI":"10.1073\/pnas.0913357107","volume":"107","author":"D Marbach","year":"2010","unstructured":"Marbach D, Prill RJ, Schaffter T, et al. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci. 2010;107(14):6286\u201391.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"1398_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/1752-0509-6-145","volume":"6","author":"AC Haury","year":"2012","unstructured":"Haury AC, Mordelet F, Vera-Licona P, et al. TIGRESS: trustful inference of gene regulation using stability selection. BMC Syst Biol. 2012;6(1):145.","journal-title":"BMC Syst Biol"},{"issue":"1","key":"1398_CR21","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/1752-0509-7-106","volume":"7","author":"J S\u0142awek","year":"2013","unstructured":"S\u0142awek J, Arod\u017a T. ENNET: inferring large gene regulatory networks from expression data using gradient boosting. BMC Syst Biol. 2013;7(1):106.","journal-title":"BMC Syst Biol"},{"issue":"3","key":"1398_CR22","doi-asserted-by":"publisher","first-page":"e92709","DOI":"10.1371\/journal.pone.0092709","volume":"9","author":"J Ruyssinck","year":"2014","unstructured":"Ruyssinck J, Geurts P, Dhaene T, et al. Nimefi: gene regulatory network inference using multiple ensemble feature importance algorithms. PLoS One. 2014;9(3):e92709.","journal-title":"PLoS One"},{"issue":"2","key":"1398_CR23","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodology. 2005;67(2):301\u201320.","journal-title":"J R Stat Soc Series B Stat Methodology"},{"issue":"8","key":"1398_CR24","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1038\/nmeth.2016","volume":"9","author":"D Marbach","year":"2012","unstructured":"Marbach D, Costello JC, K\u00fcffner R, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9(8):796\u2013804.","journal-title":"Nat Methods"},{"issue":"2","key":"1398_CR25","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1089\/cmb.2008.09TT","volume":"16","author":"D Marbach","year":"2009","unstructured":"Marbach D, Schaffter T, Mattiussi C, et al. Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J Comput Biol. 2009;16(2):229\u201339.","journal-title":"J Comput Biol"},{"key":"1398_CR26","unstructured":"The DREAM4 In Silico network challenge. http:\/\/www.synapse.org\/#!Synapse:syn3049712\/files\/."},{"key":"1398_CR27","unstructured":"The DREAM5 network challenge. http:\/\/www.synapse.org\/#!Synapse:syn2787209\/files\/."},{"key":"1398_CR28","series-title":"The annals of statistics","first-page":"1436","volume-title":"High-dimensional graphs and variable selection with the lasso","author":"N Meinshausen","year":"2006","unstructured":"Meinshausen N, B\u00fchlmann P. High-dimensional graphs and variable selection with the lasso, The annals of statistics. 2006. p. 1436\u201362."},{"issue":"1","key":"1398_CR29","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/THMS.2013.2288777","volume":"44","author":"W You","year":"2014","unstructured":"You W, Yang Z, Yuan M, et al. Totalpls: Local dimension reduction for multicategory microarray data. Human-Machine Systems, IEEE Transactions on. 2014;44(1):125\u201338.","journal-title":"Human-Machine Systems, IEEE Transactions on"},{"issue":"7","key":"1398_CR30","doi-asserted-by":"publisher","first-page":"e102541","DOI":"10.1371\/journal.pone.0102541","volume":"9","author":"S Sun","year":"2014","unstructured":"Sun S, Peng Q, Shakoor A. A kernel-based multivariate feature selection method for microarray data classification [J]. PLoS One. 2014;9(7):e102541.","journal-title":"PLoS One"},{"issue":"3","key":"1398_CR31","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1002\/cem.785","volume":"17","author":"M Barker","year":"2003","unstructured":"Barker M, Rayens W. Partial least squares for discrimination. J Chemometr. 2003;17(3):166\u201373.","journal-title":"J Chemometr"},{"key":"1398_CR32","unstructured":"Wold H, Lyttkens E. Nonlinear iterative partial least squares (NIPALS) estimation procedures. Bull Int Stat Inst. 1969;43(1)."},{"issue":"3","key":"1398_CR33","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0169-7439(93)85002-X","volume":"18","author":"S De Jong","year":"1993","unstructured":"De Jong S. SIMPLS: an alternative approach to partial least squares regression. Chemom Intel Lab Syst. 1993;18(3):251\u201363.","journal-title":"Chemom Intel Lab Syst"},{"key":"1398_CR34","first-page":"523","volume":"1","author":"S Wold","year":"1993","unstructured":"Wold S, Johansson E, Cocchi M. PLS\u2014partial least squares projections to latent structures. 3D QSAR in drug design. 1993;1:523\u201350.","journal-title":"3D QSAR in drug design"},{"issue":"16","key":"1398_CR35","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1093\/bioinformatics\/btr373","volume":"27","author":"T Schaffter","year":"2011","unstructured":"Schaffter T, Marbach D, Floreano D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics. 2011;27(16):2263\u201370.","journal-title":"Bioinformatics"},{"issue":"suppl 1","key":"1398_CR36","doi-asserted-by":"publisher","first-page":"D98","DOI":"10.1093\/nar\/gkq1110","volume":"39","author":"S Gama-Castro","year":"2011","unstructured":"Gama-Castro S, Salgado H, Peralta-Gil M, et al. RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic Acids Res. 2011;39 suppl 1:D98\u2013D105.","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"1398_CR37","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1093\/bib\/4.3.228","volume":"4","author":"SY Kim","year":"2003","unstructured":"Kim SY, Imoto S, Miyano S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform. 2003;4(3):228\u201335.","journal-title":"Brief Bioinform"},{"issue":"1","key":"1398_CR38","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1111\/j.1749-6632.2008.03756.x","volume":"1158","author":"B Di Camillo","year":"2009","unstructured":"Di Camillo B, Toffolo G, Cobelli C. A gene network simulator to assess reverse engineering algorithms. Ann N Y Acad Sci. 2009;1158(1):125\u201342.","journal-title":"Ann N Y Acad Sci"},{"issue":"1","key":"1398_CR39","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/1471-2105-7-43","volume":"7","author":"T Van den Bulcke","year":"2006","unstructured":"Van den Bulcke T, Van Leemput K, Naudts B, et al. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinf. 2006;7(1):43.","journal-title":"BMC Bioinf."},{"issue":"suppl 2","key":"1398_CR40","doi-asserted-by":"publisher","first-page":"ii122","DOI":"10.1093\/bioinformatics\/btg1069","volume":"19","author":"P Mendes","year":"2003","unstructured":"Mendes P, Sha W, Ye K. Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics. 2003;19 suppl 2:ii122\u20139.","journal-title":"Bioinformatics"},{"issue":"1","key":"1398_CR41","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1186\/1471-2105-9-461","volume":"9","author":"PE Meyer","year":"2008","unstructured":"Meyer PE, Lafitte F, Bontempi G. minet: AR\/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinf. 2008;9(1):461.","journal-title":"BMC Bioinf."},{"issue":"1","key":"1398_CR42","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, et al. Gene Ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25\u20139.","journal-title":"Nat Genet"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1398-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-016-1398-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1398-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T18:23:01Z","timestamp":1706811781000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-016-1398-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,28]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["1398"],"URL":"https:\/\/doi.org\/10.1186\/s12859-016-1398-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,28]]},"assertion":[{"value":"12 April 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"545"}}