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10.1186/1471-2105-14-S19-S2 http://scihub22266oqcxt.onion/10.1186/1471-2105-14-S19-S2 C4067946!4067946 !24564335     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24564335 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2013 ; 14 Suppl 19 (Suppl 19 ): S2 Nephropedia Template TP {{tp|p=24564335 |t=2013. iVUN: interactive Visualization of Uncertain biochemical reaction Networks |pdf=|usr=}}{{24564335 }} gab.com Text iVUN: interactive Visualization of Uncertain biochemical reaction Networks JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24564335 #FOAvip BACKGROUND: Mathematical models are nowadays widely used to describe biochemical reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as reaction fluxes or transient dynamics of biological species. METHODS AND RESULTS: We developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling. CONCLUSION: Our case study showed that iVUN can be used to perform an in-depth study of biochemical reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here. Twit Text FOAVip iVUN: interactive Visualization of Uncertain biochemical reaction Networks ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24564335 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24564335 #FOAvip English Wikipedia <ref>{{Cite pmid|24564335 }}</ref> iVUN: interactive Visualization of Uncertain biochemical reaction Networks #MMPMID24564335 Vehlow C ; Hasenauer J ; Kramer A ; Raue A ; Hug S ; Timmer J ; Radde N ; Theis FJ ; Weiskopf D BMC Bioinformatics 2013[]; 14 Suppl 19 (Suppl 19 ): S2 PMID24564335 show ga BACKGROUND: Mathematical models are nowadays widely used to describe biochemical reaction networks. One of the main reasons for this is that models facilitate the integration of a multitude of different data and data types using parameter estimation. Thereby, models allow for a holistic understanding of biological processes. However, due to measurement noise and the limited amount of data, uncertainties in the model parameters should be considered when conclusions are drawn from estimated model attributes, such as reaction fluxes or transient dynamics of biological species. METHODS AND RESULTS: We developed the visual analytics system iVUN that supports uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks modeled by ordinary differential equations. The multivariate graph of the network is visualized as a node-link diagram, and statistics of the attributes are mapped to the color of nodes and links of the graph. In addition, the graph view is linked with several views, such as line plots, scatter plots, and correlation matrices, to support locating uncertainties and the analysis of their time dependencies. As demonstration, we use iVUN to quantitatively analyze the dynamics of a model for Epo-induced JAK2/STAT5 signaling. CONCLUSION: Our case study showed that iVUN can be used to perform an in-depth study of biochemical reaction networks, including attribute uncertainties, correlations between these attributes and their uncertainties as well as the attribute dynamics. In particular, the linking of different visualization options turned out to be highly beneficial for the complex analysis tasks that come with the biological systems as presented here. |*Models, Biological [MESH] |*Models, Chemical [MESH] |*Uncertainty [MESH] |Computational Biology/methods [MESH] |Computer Graphics [MESH] |Metabolic Networks and Pathways [MESH]iVUN: interactive Visualization of Uncertain biochemical reaction Netw(epmc).pdf DeepDyve Pubget Overpricing