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10.1093/bioinformatics/btt751 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btt751 C3982155!3982155!24403541     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2014 ; 30 (8): 1163-71 Nephropedia Template TP {{tp|p=24403541|t=2014. Prediction of therapeutic microRNA based on the human metabolic network |pdf=|usr=}}{{24403541}} gab.com Text Prediction of therapeutic microRNA based on the human metabolic network JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24403541 #FOAvip Motivation: MicroRNA (miRNA) expression has been found to be deregulated in human cancer, contributing, in part, to the interest of the research community in using miRNAs as alternative therapeutic targets. Although miRNAs could be potential targets, identifying which miRNAs to target for a particular type of cancer has been difficult due to the limited knowledge on their regulatory roles in cancer. We address this challenge by integrating miRNA-target prediction, metabolic modeling and context-specific gene expression data to predict therapeutic miRNAs that could reduce the growth of cancer.Results: We developed a novel approach to simulate a condition-specific metabolic system for human hepatocellular carcinoma (HCC) wherein overexpression of each miRNA was simulated to predict their ability to reduce cancer cell growth. Our approach achieved >80% accuracy in predicting the miRNAs that could suppress metastasis and progression of liver cancer based on various experimental evidences in the literature. This condition-specific metabolic system provides a framework to explore the mechanisms by which miRNAs modulate metabolic functions to affect cancer growth. To the best of our knowledge, this is the first computational approach implemented to predict therapeutic miRNAs for human cancer based on their functional role in cancer metabolism. Analyzing the metabolic functions altered by the miRNA-identified metabolic genes essential for cell growth and proliferation that are targeted by the miRNAs.Availability and implementation: See supplementary protocols and http://www.egr.msu.edu/changroup/Protocols%20Index.html.Contact:krischan@egr.msu. eduSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Prediction of therapeutic microRNA based on the human metabolic network ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24403541 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24403541 #FOAvip English Wikipedia <ref>{{Cite pmid|24403541}}</ref> Prediction of therapeutic microRNA based on the human metabolic network #MMPMID24403541 Wu M; Chan C Bioinformatics 2014[Apr]; 30 (8): 1163-71 PMID24403541show ga Motivation: MicroRNA (miRNA) expression has been found to be deregulated in human cancer, contributing, in part, to the interest of the research community in using miRNAs as alternative therapeutic targets. Although miRNAs could be potential targets, identifying which miRNAs to target for a particular type of cancer has been difficult due to the limited knowledge on their regulatory roles in cancer. We address this challenge by integrating miRNA-target prediction, metabolic modeling and context-specific gene expression data to predict therapeutic miRNAs that could reduce the growth of cancer.Results: We developed a novel approach to simulate a condition-specific metabolic system for human hepatocellular carcinoma (HCC) wherein overexpression of each miRNA was simulated to predict their ability to reduce cancer cell growth. Our approach achieved >80% accuracy in predicting the miRNAs that could suppress metastasis and progression of liver cancer based on various experimental evidences in the literature. This condition-specific metabolic system provides a framework to explore the mechanisms by which miRNAs modulate metabolic functions to affect cancer growth. To the best of our knowledge, this is the first computational approach implemented to predict therapeutic miRNAs for human cancer based on their functional role in cancer metabolism. Analyzing the metabolic functions altered by the miRNA-identified metabolic genes essential for cell growth and proliferation that are targeted by the miRNAs.Availability and implementation: See supplementary protocols and http://www.egr.msu.edu/changroup/Protocols%20Index.html.Contact:krischan@egr.msu. eduSupplementary information:Supplementary data are available at Bioinformatics online. äPrediction of therapeutic microRNA based on the human metabolic networ(epmc).pdf DeepDyve Pubget Overpricing