caa a22 4500 605478511 CHVBK 20210128100406.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s10994-014-5479-3 doi (NATIONALLICENCE)springer-10.1007/s10994-014-5479-3 Operator-valued kernel-based vector autoregressive models for network inference [Elektronische Daten] [Néhémy Lim, Florence d'Alché-Buc, Cédric Auliac, George Michailidis] Reverse-engineering of high-dimensional dynamical systems from time-course data still remains a challenging and important problem in knowledge discovery. For this learning task, a number of approaches primarily based on sparse linear models or Granger causality concepts have been proposed in the literature. However, when a system exhibits nonlinear dynamics, there does not exist a systematic approach that takes into account the nature of the underlying system. In this work, we introduce a novel family of vector autoregressive models based on different operator-valued kernels to identify the dynamical system and retrieve the target network that characterizes the interactions of its components. Assuming a sparse underlying structure, a key challenge, also present in the linear case, is to control the model's sparsity. This is achieved through the joint learning of the structure of the kernel and the basis vectors. To solve this learning task, we propose an alternating optimization algorithm based on proximal gradient procedures that learns both the structure of the kernel and the basis vectors. Results on the DREAM3 competition gene regulatory benchmark networks of sizes 10 and 100 show the new model outperforms existing methods. Another application of the model on climate data identifies interesting and interpretable interactions between natural and human activity factors, thus confirming the ability of the learning scheme to retrieve dependencies between state-variables. The Author(s), 2014 Network inference nationallicence Operator-valued kernel nationallicence Regularization nationallicence Proximal gradient methods nationallicence Vector autoregressive model nationallicence Jacobian nationallicence Lim Néhémy CEA, LIST, 91191, Gif-sur-Yvette Cedex, France aut d'Alché-Buc Florence IBISC EA 4526, Université d'Évry-Val d'Essonne, 91000, Évry, France aut Auliac Cédric CEA, LIST, 91191, Gif-sur-Yvette Cedex, France aut Michailidis George Department of Statistics, University of Michigan, 48109-1107, Ann Arbor, MI, USA aut Machine Learning Springer US; http://www.springer-ny.com 99/3(2015-06-01), 489-513 0885-6125 99:3<489 2015 99 10994 https://doi.org/10.1007/s10994-014-5479-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-014-5479-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lim Néhémy CEA, LIST, 91191, Gif-sur-Yvette Cedex, France aut NATIONALLICENCE 700 1- d'Alché-Buc Florence IBISC EA 4526, Université d'Évry-Val d'Essonne, 91000, Évry, France aut NATIONALLICENCE 700 1- Auliac Cédric CEA, LIST, 91191, Gif-sur-Yvette Cedex, France aut NATIONALLICENCE 700 1- Michailidis George Department of Statistics, University of Michigan, 48109-1107, Ann Arbor, MI, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 99/3(2015-06-01), 489-513 0885-6125 99:3<489 2015 99 10994