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10.1186/s12859-016-1136-0 http://scihub22266oqcxt.onion/10.1186/s12859-016-1136-0 C4919861!4919861!27342572     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Bioinformatics 2016 ; 17 (ä): ä Nephropedia Template TP {{tp|p=27342572|t=2016. Consistency of biological networks inferred from microarray and sequencing data |pdf=|usr=}}{{27342572}} gab.com Text Consistency of biological networks inferred from microarray and sequencing data JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27342572 #FOAvip Background: Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Results: Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges. Conclusions: Careful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-1136-0) contains supplementary material, which is available to authorized users. Twit Text FOAVip Consistency of biological networks inferred from microarray and sequencing data ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27342572 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27342572 #FOAvip English Wikipedia <ref>{{Cite pmid|27342572}}</ref> Consistency of biological networks inferred from microarray and sequencing data #MMPMID27342572 Vinciotti V; Wit EC; Jansen R; de Geus EJCN; Penninx BWJH; Boomsma DI; ?t Hoen PAC BMC Bioinformatics 2016[]; 17 (ä): ä PMID27342572show ga Background: Sparse Gaussian graphical models are popular for inferring biological networks, such as gene regulatory networks. In this paper, we investigate the consistency of these models across different data platforms, such as microarray and next generation sequencing, on the basis of a rich dataset containing samples that are profiled under both techniques as well as a large set of independent samples. Results: Our analysis shows that individual node variances can have a remarkable effect on the connectivity of the resulting network. Their inconsistency across platforms and the fact that the variability level of a node may not be linked to its regulatory role mean that, failing to scale the data prior to the network analysis, leads to networks that are not reproducible across different platforms and that may be misleading. Moreover, we show how the reproducibility of networks across different platforms is significantly higher if networks are summarised in terms of enrichment amongst functional groups of interest, such as pathways, rather than at the level of individual edges. Conclusions: Careful pre-processing of transcriptional data and summaries of networks beyond individual edges can improve the consistency of network inference across platforms. However, caution is needed at this stage in the (over)interpretation of gene regulatory networks inferred from biological data. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-016-1136-0) contains supplementary material, which is available to authorized users. äConsistency of biological networks inferred from microarray and sequen(epmc).pdf DeepDyve Pubget Overpricing