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10.1186/s13040-017-0136-6 http://scihub22266oqcxt.onion/10.1186/s13040-017-0136-6 C5420410!5420410!28484519     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BioData+Min 2017 ; 10 (ä): ä Nephropedia Template TP {{tp|p=28484519|t=2017. Study of Meta-analysis strategies for network inference using information-theoretic approaches |pdf=|usr=}}{{28484519}} gab.com Text Study of Meta-analysis strategies for network inference using information-theoretic approaches JO: BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28484519 #FOAvip Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets.To date, there are mainly two strategies for the problem of interest: the first one (?data merging?) merges all datasets together and then infers a GRN whereas the other (?networks ensemble?) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones. Twit Text FOAVip Study of Meta-analysis strategies for network inference using information-theoretic approaches ????BioData Min http://www.kidney.de/pget.php?&tw=1&pmid=28484519 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28484519 #FOAvip English Wikipedia <ref>{{Cite pmid|28484519}}</ref> Study of Meta-analysis strategies for network inference using information-theoretic approaches #MMPMID28484519 Pham NC; Haibe-Kains B; Bellot P; Bontempi G; Meyer PE BioData Min 2017[]; 10 (ä): ä PMID28484519show ga Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches, which suffer from experimental biases and the low number of samples by analysing individual datasets.To date, there are mainly two strategies for the problem of interest: the first one (?data merging?) merges all datasets together and then infers a GRN whereas the other (?networks ensemble?) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. Results: In this work, we are going to present another meta-analysis approach for inferring GRNs from multiple studies. Our proposed meta-analysis approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix. Afterwards, we evaluate the performance of the two commonly used approaches mentioned above and our presented approach with a systematic set of experiments based on in silico benchmarks. Conclusions: We proposed a first systematic evaluation of different strategies for reverse engineering GRNs from multiple datasets. Experiment results strongly suggest that assembling matrices of pairwise dependencies is a better strategy for network inference than the two commonly used ones. äStudy of Meta-analysis strategies for network inference using informat(epmc).pdf DeepDyve Pubget Overpricing