01654nas a2200169   4500008004100000022001400041245009000055210006900145260001300214300001100227490000800238520105800246100001701304700002301321700001801344856012201362       2012                        eng d  a1611-753000aIntegrating heterogeneous gene expression data for gene regulatory network modelling.0 aIntegrating heterogeneous gene expression data for gene regulato  c2012 Jun  a95-1020 v1313 a<p>Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/integrating-heterogeneous-gene-expression-data-gene-regulatory-network-modelling