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10.1186/s12859-017-1567-2 http://scihub22266oqcxt.onion/10.1186/s12859-017-1567-2 C5335754!5335754!28253853     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28253853&cmd=llinks): Failed to open stream: HTTP request failed! 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Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network |pdf=|usr=}}{{28253853}} gab.com Text Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28253853 #FOAvip Background: With the advancement of high-throughput technologies and enrichment of popular public databases, more and more research focuses of bioinformatics research have been on computational integration of network and gene expression profiles for extracting context-dependent active subnetworks. Many methods for subnetwork searching have been developed. Scoring and searching algorithms present a range of computational considerations and implementations. The primary goal of present study is to comprehensively evaluate the performance of different subnetwork detection methods. Eleven popular methods were selected for comprehensive comparison. Results: First, taking into account the dependence of genes given a protein-protein interaction (PPI) network, we simulated microarray gene expression data under case and control conditions. Then each method was applied to the simulated data for subnetwork identification. Second, a large microarray data set of prostate cancer was used to assess the practical performance of each method. Using both simulation studies and a real data application, we evaluated the performance of different methods in terms of recall and precision. Conclusions: jActiveModules, PinnacleZ and WMAXC performed well in identifying subnetwork with relative high precision and recall. BioNet performed very well only in precision. As none of methods outperformed other methods overall, users should choose an appropriate method based on the purposes of their studies. Twit Text FOAVip Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28253853 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28253853 #FOAvip English Wikipedia <ref>{{Cite pmid|28253853}}</ref> Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network #MMPMID28253853 He H; Lin D; Zhang J; Wang Yp; Deng Hw BMC Bioinformatics 2017[]; 18 (ä): ä PMID28253853show ga Background: With the advancement of high-throughput technologies and enrichment of popular public databases, more and more research focuses of bioinformatics research have been on computational integration of network and gene expression profiles for extracting context-dependent active subnetworks. Many methods for subnetwork searching have been developed. Scoring and searching algorithms present a range of computational considerations and implementations. The primary goal of present study is to comprehensively evaluate the performance of different subnetwork detection methods. Eleven popular methods were selected for comprehensive comparison. Results: First, taking into account the dependence of genes given a protein-protein interaction (PPI) network, we simulated microarray gene expression data under case and control conditions. Then each method was applied to the simulated data for subnetwork identification. Second, a large microarray data set of prostate cancer was used to assess the practical performance of each method. Using both simulation studies and a real data application, we evaluated the performance of different methods in terms of recall and precision. Conclusions: jActiveModules, PinnacleZ and WMAXC performed well in identifying subnetwork with relative high precision and recall. BioNet performed very well only in precision. As none of methods outperformed other methods overall, users should choose an appropriate method based on the purposes of their studies. äComparison of statistical methods for subnetwork detection in the inte(epmc).pdf DeepDyve Pubget Overpricing