{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T07:28:06Z","timestamp":1778657286004,"version":"3.51.4"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1271,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure.<\/jats:p>\n               <jats:p>Results: A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model\u2019s Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches.<\/jats:p>\n               <jats:p>Availability: The OKVAR-Boost Matlab code is available as the archive: http:\/\/amis-group.fr\/sourcecode-okvar-boost\/OKVARBoost-v1.0.zip.<\/jats:p>\n               <jats:p>Contact: \u00a0florence.dalche@ibisc.univ-evry.fr<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt167","type":"journal-article","created":{"date-parts":[[2013,4,11]],"date-time":"2013-04-11T02:41:33Z","timestamp":1365648093000},"page":"1416-1423","source":"Crossref","is-referenced-by-count":27,"title":["OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks"],"prefix":"10.1093","volume":"29","author":[{"given":"N\u00e9h\u00e9my","family":"Lim","sequence":"first","affiliation":[{"name":"1 IBISC EA 4526, Universit\u00e9 d\u2019\u00c9vry-Val d\u2019Essonne, 91000 \u00c9vry, France, 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA, 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA and 4AMIB\/TAO, INRIA-Saclay, LRI umr CNRS 8623, Universit\u00e9 Paris Sud, 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasin","family":"\u015eenbabao\u011flu","sequence":"additional","affiliation":[{"name":"1 IBISC EA 4526, Universit\u00e9 d\u2019\u00c9vry-Val d\u2019Essonne, 91000 \u00c9vry, France, 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA, 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA and 4AMIB\/TAO, INRIA-Saclay, LRI umr CNRS 8623, Universit\u00e9 Paris Sud, 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Michailidis","sequence":"additional","affiliation":[{"name":"1 IBISC EA 4526, Universit\u00e9 d\u2019\u00c9vry-Val d\u2019Essonne, 91000 \u00c9vry, France, 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA, 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA and 4AMIB\/TAO, INRIA-Saclay, LRI umr CNRS 8623, Universit\u00e9 Paris Sud, 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florence","family":"d\u2019Alch\u00e9-Buc","sequence":"additional","affiliation":[{"name":"1 IBISC EA 4526, Universit\u00e9 d\u2019\u00c9vry-Val d\u2019Essonne, 91000 \u00c9vry, France, 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA, 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA and 4AMIB\/TAO, INRIA-Saclay, LRI umr CNRS 8623, Universit\u00e9 Paris Sud, 91400 Orsay, France"},{"name":"1 IBISC EA 4526, Universit\u00e9 d\u2019\u00c9vry-Val d\u2019Essonne, 91000 \u00c9vry, France, 2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA, 3Department of Statistics, University of Michigan, Ann Arbor, MI 48109-1107, USA and 4AMIB\/TAO, INRIA-Saclay, LRI umr CNRS 8623, Universit\u00e9 Paris Sud, 91400 Orsay, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,4,10]]},"reference":[{"key":"2023062610163934400_btt167-B1","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1093\/bioinformatics\/btp511","article-title":"Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics","volume":"25","author":"\u00c4ij\u00f6","year":"2009","journal-title":"Bioinformatics"},{"key":"2023062610163934400_btt167-B2","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1093\/bioinformatics\/btp485","article-title":"A boosting approach to structure learning of graphs with and without prior knowledge","volume":"25","author":"Anjum","year":"2009","journal-title":"Bioinformatics"},{"key":"2023062610163934400_btt167-B3","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/msb4100120","article-title":"How to infer gene networks from expression profiles","volume":"3","author":"Bansal","year":"2007","journal-title":"Mol. 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