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Ivan Vujacic

  • room number:
    444 (Bernoulliborg, building 5161)
  • phone numbers:
    domestic: 050 363 3536
    international: +31 50 363 3536
    fax: 050 363 3800
  • e-mail: i.vujacic@rug.nl
  • My CV: Download PDF

Refereed Journal Publications

1. Ivan Vujacic, Itai Dattner, Javier Gonzalez, Ernst Wit (2014) Time-course window estimator for ordinary differential equations linear in the parameters. Statistics and Computing (accepted; under revision)

2. Javier Gonzalez, Ivan Vujacic, Ernst Wit (2014) Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations. Pattern Recognition Letters. http://www.sciencedirect.com/science/article/pii/S0167865514000695

3. Javier Gonzalez, Ivan Vujacic, Ernst Wit (2013) Inferring latent gene regulatory network kinetics. Statistical applications in genetics and molecular biology. http://www.degruyter.com/view/j/sagmb.2013.12.issue-1/sagmb-2012-0006/sagmb-2012-0006.xml

Unpublished manuscripts

1.Ivan Vujacic, Antonino Abbruzzo, Ernst Wit (2014) A computationally fast alternative to cross-validation in penalized Gaussian graphical models. http://arxiv.org/abs/1309.6216

2. Antonino Abbruzzo, Ivan Vujacic, Ernst Wit, Angelo M. Mineo (2014) Generalized information criterion for model selection in penalized graphical models. http://arxiv.org/abs/1403.1249

Refereed Conference Papers

1. Javier González, Ivan Vujacic and Ernst Wit. Reproducing kernel Hilbert space based estimation of ODE models in system biology. (PRIB'13) Proceedings of the Eighth IAPR International Conference on Pattern Recognition in Bioinformatics, 2013.

2. Ernst Wit, Ivan Vujacic, Javier González. Inference of non-linear ODE dynamics. (IWSM'13) Proceedings of the 28th International Workshop on Statistical Modelling, (Muggeo VMR, Capursi V, Boscaino G, Lovison G, editors), vol.2, pp. 465-474, 2013.

3. Antonino Abbruzzo, Ivan Vujacic, Ernst Wit, Angelo M. Mineo. Model Selection for penalized Gaussian Graphical Models. (IWSM'13) Proceedings of the 28th International Workshop on Statistical Modelling, (Muggeo VMR, Capursi V, Boscaino G, Lovison G, editors), vol.1, pp. 59-65, 2013.

R package

oderkhs_1.0.tar.gz,oderkhs.pdf: oderkhs, R-package that estimates parameters of Ordinary Differential Equations measured with noise by using Reproducing Kernel Hilbert Space (RKHS) approach.