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Apeldoorn, Netherlands

Menendez P.,Wageningen University | Menendez P.,Center for BioSystems Genetics | Kourmpetis Y.A.I.,Wageningen University | ter Braak C.J.F.,Wageningen University | And 2 more authors.
PLoS ONE | Year: 2010

A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network. Due to the static character of the challenge data, we tackled the problem via a sparse Gaussian Markov Random Field, which relates network topology with the covariance inverse generated by the gene measurements. As for the computations, we used the Graphical Lasso algorithm which provided a large range of candidate network topologies. The main task was to select the optimal network topology and for that, different model selection criteria were explored. The selected networks were compared with the golden standards and the results ranked using the scoring metrics applied in the challenge, giving a better insight in our submission and the way to improve it. Our approach provides an easy statistical and computational framework to infer gene regulatory networks that is suitable for large networks, even if the number of the observations (perturbations) is greater than the number of variables (genes). © 2010 Menendez et al. Source

Menendez P.,Wageningen University | Menendez P.,Center for BioSystems Genetics | Eilers P.,Wageningen University | Eilers P.,Erasmus Medical Center | And 6 more authors.
Euphytica | Year: 2012

The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models. © 2011 The Author(s). Source

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