{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:59:50Z","timestamp":1773849590322,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2014,12,17]],"date-time":"2014-12-17T00:00:00Z","timestamp":1418774400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2015,6]]},"DOI":"10.1007\/s10994-014-5479-3","type":"journal-article","created":{"date-parts":[[2014,12,16]],"date-time":"2014-12-16T12:21:15Z","timestamp":1418732475000},"page":"489-513","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Operator-valued kernel-based vector autoregressive models for network inference"],"prefix":"10.1007","volume":"99","author":[{"given":"N\u00e9h\u00e9my","family":"Lim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florence","family":"d\u2019Alch\u00e9-Buc","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9dric","family":"Auliac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Michailidis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,12,17]]},"reference":[{"issue":"22","key":"5479_CR1","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1093\/bioinformatics\/btp511","volume":"25","author":"T Aijo","year":"2009","unstructured":"Aijo, T., & Lahdesmaki, H. (2009). Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics. Bioinformatics, 25(22), 2937\u20132944.","journal-title":"Bioinformatics"},{"key":"5479_CR2","unstructured":"Alvarez, M. A., Rosasco, L., & Lawrence, D. N. (2011). Kernels for vector-valued functions: A review. Technical report, MIT\\_CSAIL-TR-2011-033."},{"issue":"1","key":"5479_CR3","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1471-2105-9-91","volume":"9","author":"C Auliac","year":"2008","unstructured":"Auliac, C., Frouin, V., & Gidrol, X. (2008). Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset. BMC Bioinformatics, 9(1), 91.","journal-title":"BMC Bioinformatics"},{"key":"5479_CR4","doi-asserted-by":"crossref","unstructured":"Baldassarre, L., Rosasco, L., Barla, A., & Verri, A. (2010). Vector field learning via spectral filtering. In J. Balczar, F. Bonchi, A. Gionis, & M. Sebag (Eds.), Machine learning and knowledge discovery in databases. Lecture notes in computer science (Vol. 6321, pp. 56\u201371). Berlin\/Heidelberg: Springer.","DOI":"10.1007\/978-3-642-15880-3_10"},{"key":"5479_CR5","first-page":"42","volume-title":"Convex optimization in signal processing and communications","author":"A Beck","year":"2010","unstructured":"Beck, A., & Teboulle, M. (2010). Gradient-based algorithms with applications to signal recovery problems. In D. Palomar & Y. Eldar (Eds.), Convex optimization in signal processing and communications (pp. 42\u201388). Cambridge: Cambridge press."},{"issue":"6","key":"5479_CR6","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1109\/TSP.2011.2129515","volume":"59","author":"A Bolstad","year":"2011","unstructured":"Bolstad, A., Van Veen, B., & Nowak, R. (2011). Causal network inference via group sparsity regularization. IEEE Trans Signal Process, 59(6), 2628\u20132641.","journal-title":"IEEE Trans Signal Process"},{"key":"5479_CR7","unstructured":"Brouard, C., d\u2019Alch\u00e9 Buc, F., & Szafranski, M. (2011). Semi-supervised penalized output kernel regression for link prediction. In ICML-2011 (pp. 593\u2013600)."},{"key":"5479_CR8","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-20192-9","volume-title":"Statistics for high-dimensional data: Methods, theory and applications","author":"P B\u00fchlmann","year":"2011","unstructured":"B\u00fchlmann, P., & van de Geer, S. (2011). Statistics for high-dimensional data: Methods, theory and applications. Berlin: Springer."},{"key":"5479_CR9","unstructured":"Caponnetto, A., Micchelli, C. A., Pontil, M., & Ying, Y. (2008). Universal multitask kernels. The Journal of Machine Learning Research, 9, 1615\u20131646."},{"key":"5479_CR10","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., & Ganguly, A. R. (2012). Sparse group lasso: Consistency and climate applications. In SDM (pp. 47\u201358). SIAM\/Omnipress","DOI":"10.1137\/1.9781611972825.5"},{"issue":"2","key":"5479_CR11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.mbs.2009.03.002","volume":"219","author":"I Chou","year":"2009","unstructured":"Chou, I., & Voit, E. O. (2009). Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences, 219(2), 57\u201383.","journal-title":"Mathematical Biosciences"},{"key":"5479_CR12","doi-asserted-by":"crossref","unstructured":"Combettes, P. L., & Pesquet, J. C. (2011). Proximal splitting methods in signal processing. In Fixed-point algorithms for inverse problems in science and engineering. Springer Optimization and Its Applications, Vol. 49, pp. 185\u2013212.","DOI":"10.1007\/978-1-4419-9569-8_10"},{"key":"5479_CR13","unstructured":"Dinuzzo, F., & Fukumizu, K. (2011). Learning low-rank output kernels. In Proceedings of the 3rd Asian conference on machine learning, JMLR: Workshop and conference proceedings, Vol. 20."},{"issue":"2","key":"5479_CR14","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10994-012-5311-x","volume":"90","author":"F Dondelinger","year":"2013","unstructured":"Dondelinger, F., L\u00e8bre, S., & Husmeier, D. (2013). Non-homogeneous dynamic bayesian networks with bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Machine Learning Journal, 90(2), 191\u2013230.","journal-title":"Machine Learning Journal"},{"issue":"5659","key":"5479_CR15","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1126\/science.1094068","volume":"303","author":"N Friedman","year":"2004","unstructured":"Friedman, N. (2004). Inferring cellular networks using probabilistic graphical models. Science, 303(5659), 799\u2013805.","journal-title":"Science"},{"issue":"4","key":"5479_CR16","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.jmoneco.2009.03.017","volume":"56","author":"S Gilchrist","year":"2009","unstructured":"Gilchrist, S., Yankov, V., & Zakraj\u0161ek, E. (2009). Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets. Journal of Monetary Economics, 56(4), 471\u2013493.","journal-title":"Journal of Monetary Economics"},{"issue":"5","key":"5479_CR17","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1038\/nbt0505-554","volume":"23","author":"A Hartemink","year":"2005","unstructured":"Hartemink, A. (2005). Reverse engineering gene regulatory networks. Nat Biotechnol, 23(5), 554\u2013555.","journal-title":"Nat Biotechnol"},{"issue":"23","key":"5479_CR18","doi-asserted-by":"crossref","first-page":"4453","DOI":"10.1016\/j.ins.2008.07.029","volume":"178","author":"H Iba","year":"2008","unstructured":"Iba, H. (2008). Inference of differential equation models by genetic programming. Information Sciences, 178(23), 4453\u20134468.","journal-title":"Information Sciences"},{"key":"5479_CR19","unstructured":"Kadri, H., Rabaoui, A., Preux, P., Duflos, E., & Rakotomamonjy, A. (2011). Functional regularized least squares classication with operator-valued kernels. In ICML-2011 (pp 993\u20131000)."},{"key":"5479_CR20","doi-asserted-by":"crossref","unstructured":"Kolaczyk, E. D. (2009). Statistical analysis of network data: Methods and models: Series in Statistics. Berlin: Springer.","DOI":"10.1007\/978-0-387-88146-1"},{"issue":"6","key":"5479_CR21","doi-asserted-by":"crossref","first-page":"061,916+","DOI":"10.1103\/PhysRevE.79.061916","volume":"79","author":"MA Kramer","year":"2009","unstructured":"Kramer, M. A., Eden, U. T., Cash, S. S., & Kolaczyk, E. D. (2009). Network inference with confidence from multivariate time series. Physical Review E, 79(6), 061,916+.","journal-title":"Physical Review E"},{"key":"5479_CR22","unstructured":"Lawrence, N., Girolami, M., Rattray, M., & Sanguinetti, G. (Eds.) (2010). Learning and inference in computational systems biology. Cambridge: MIT Press."},{"issue":"1","key":"5479_CR23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2202\/1544-6115.1294","volume":"8","author":"S L\u00e8bre","year":"2009","unstructured":"L\u00e8bre, S. (2009). Inferring dynamic genetic networks with low order independencies. Statistical Applications in Genetics and Molecular Biology, 8(1), 1\u201338.","journal-title":"Statistical Applications in Genetics and Molecular Biology"},{"issue":"11","key":"5479_CR24","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1093\/bioinformatics\/btt167","volume":"29","author":"N Lim","year":"2013","unstructured":"Lim, N., Senbabaoglu, Y., & Michailidis, G. (2013). OKVAR-Boost: A novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks. Bioinformatics, 29(11), 1416\u20131423.","journal-title":"Bioinformatics"},{"key":"5479_CR25","unstructured":"Liu, Y., Niculescu-Mizil, A., & Lozano, A. (2010). Learning temporal causal graphs for relational time-series analysis. In J. F\u00fcrnkranz, & T. Joachims (Eds.), ICML-2010."},{"key":"5479_CR26","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1038\/nmeth0410-247","volume":"7","author":"M Maathuis","year":"2010","unstructured":"Maathuis, M., Colombo, D., Kalish, M., & B\u00fchlmann, P. (2010). Predicting causal effects in large-scale systems from observational data. Nature Methods, 7, 247\u2013248.","journal-title":"Nature Methods"},{"issue":"Suppl 1","key":"5479_CR27","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","volume":"7","author":"I Margolin","year":"2006","unstructured":"Margolin, I., & Nemenman, Aand. (2006). Aracne: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7(Suppl 1), S7.","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"5479_CR28","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1186\/1471-2105-10-448","volume":"10","author":"J Mazur","year":"2009","unstructured":"Mazur, J., Ritter, D., Reinelt, G., & Kaderali, L. (2009). Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling. BMC Bioinformatics, 10(1), 448.","journal-title":"BMC Bioinformatics"},{"key":"5479_CR29","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1214\/009053606000000281","volume":"34","author":"N Meinshausen","year":"2006","unstructured":"Meinshausen, N., & B\u00fchlmann, P. (2006). High dimensional graphs and variable selection with the lasso. Annals of Statistics, 34, 1436\u20131462.","journal-title":"Annals of Statistics"},{"key":"5479_CR30","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1162\/0899766052530802","volume":"17","author":"CA Micchelli","year":"2005","unstructured":"Micchelli, C. A., & Pontil, M. A. (2005). On learning vector-valued functions. Neural Computation, 17, 177\u2013204.","journal-title":"Neural Computation"},{"issue":"4","key":"5479_CR31","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1080\/10618600.2012.738614","volume":"21","author":"G Michailidis","year":"2012","unstructured":"Michailidis, G. (2012). Statistical challenges in biological networks. Journal of Computational and Graphical Statistics, 21(4), 840\u2013855.","journal-title":"Journal of Computational and Graphical Statistics"},{"issue":"2","key":"5479_CR32","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.mbs.2013.10.003","volume":"246","author":"G Michailidis","year":"2013","unstructured":"Michailidis, G., & d\u2019Alch\u00e9 Buc, F. (2013). Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues. Mathematical Biosciences, 246(2), 326\u2013334.","journal-title":"Mathematical Biosciences"},{"key":"5479_CR33","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511762888","volume-title":"Experimental political science and the study of causality","author":"R Morton","year":"2010","unstructured":"Morton, R., & Williams, K. C. (2010). Experimental political science and the study of causality. Cambridge: Cambridge University Press."},{"key":"5479_CR34","unstructured":"Murphy, K. P. (1998). Dynamic bayesian networks: Representation, inference and learning. PhD thesis, Computer Science, University of Berkeley, CA, USA."},{"key":"5479_CR35","unstructured":"Parry, M., Canziani, O., Palutikof, J., van der Linden, P., Hanson, C., et al. (2007). Climate change 2007: Impacts, adaptation and vulnerability. Intergovernmental Panel on Climate Change."},{"key":"5479_CR36","doi-asserted-by":"crossref","unstructured":"Perrin, B. E., Ralaivola, L., & Mazurie, A., Bottani, S., Mallet, J., d\u2019Alch\u00e9-Buc, F. (2003). Gene networks inference using dynamic bayesian networks. Bioinformatics, 19(S2), 38\u201348.","DOI":"10.1093\/bioinformatics\/btg1071"},{"issue":"2","key":"5479_CR37","doi-asserted-by":"crossref","first-page":"e9202","DOI":"10.1371\/journal.pone.0009202","volume":"5","author":"R Prill","year":"2010","unstructured":"Prill, R., Marbach, D., Saez-Rodriguez, J., Sorger, P., Alexopoulos, L., Xue, X., et al. (2010). Towards a rigorous assessment of systems biology models: The DREAM3 challenges. PLoS ONE, 5(2), e9202.","journal-title":"PLoS ONE"},{"key":"5479_CR38","unstructured":"Raguet, H., Fadili, & J., Peyr\u00e9, G. (2011). Generalized forward-backward splitting. arXiv preprint arXiv:1108.4404 ."},{"key":"5479_CR39","first-page":"1351","volume-title":"ICML-2012","author":"E Richard","year":"2012","unstructured":"Richard, E., Savalle, P. A., & Vayatis, N. (2012). Estimation of simultaneously sparse and low rank matrices. In J. Langford & J. Pineau (Eds.), ICML-2012 (pp. 1351\u20131358). New York, NY, USA: Omnipress."},{"issue":"16","key":"5479_CR40","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1093\/bioinformatics\/btr373","volume":"27","author":"T Schaffter","year":"2011","unstructured":"Schaffter, T., Marbach, D., & Floreano, D. (2011). Genenetweaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 27(16), 2263\u20132270.","journal-title":"Bioinformatics"},{"key":"5479_CR41","doi-asserted-by":"crossref","unstructured":"Senkene, E., & Tempel\u2019man, A. (1973). Hilbert spaces of operator-valued functions. Lithuanian Mathematical Journal, 13(4), 665\u2013670.","DOI":"10.1007\/BF01630739"},{"issue":"18","key":"5479_CR42","doi-asserted-by":"crossref","first-page":"i517","DOI":"10.1093\/bioinformatics\/btq377","volume":"26","author":"A Shojaie","year":"2010","unstructured":"Shojaie, A., & Michailidis, G. (2010). Discovering graphical granger causality using a truncating lasso penalty. Bioinformatics, 26(18), i517\u2013i523.","journal-title":"Bioinformatics"},{"issue":"1","key":"5479_CR43","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B, 68(1), 49\u201367.","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"1","key":"5479_CR44","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/1471-2105-10-122","volume":"10","author":"C Zou","year":"2009","unstructured":"Zou, C., & Feng, J. (2009). Granger causality vs. dynamic bayesian network inference: A comparative study. BMC Bioinformatics, 10(1), 122.","journal-title":"BMC Bioinformatics"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-014-5479-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-014-5479-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-014-5479-3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T21:40:39Z","timestamp":1559338839000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-014-5479-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,12,17]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2015,6]]}},"alternative-id":["5479"],"URL":"https:\/\/doi.org\/10.1007\/s10994-014-5479-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,12,17]]}}}