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10.1155/2014/278956 http://scihub22266oqcxt.onion/10.1155/2014/278956 C4053081!4053081 !24949431     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 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 Deprecated: Implicit conversion from float 213.6 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\24949431 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biomed+Res+Int 2014 ; 2014 (ä): 278956 Nephropedia Template TP {{tp|p=24949431 |t=2014. A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma |pdf=|usr=}}{{24949431 }} gab.com Text A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=24949431 #FOAvip In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC. Twit Text FOAVip A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=24949431 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24949431 #FOAvip English Wikipedia <ref>{{Cite pmid|24949431 }}</ref> A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma #MMPMID24949431 Zhuang L ; Wu Y ; Han J ; Ling X ; Wang L ; Zhu C ; Fu Y Biomed Res Int 2014[]; 2014 (ä): 278956 PMID24949431 show ga In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC. |Biomarkers, Tumor/*genetics/isolation & purification [MESH] |Carcinoma, Hepatocellular/*genetics/pathology [MESH] |Gene Expression Profiling [MESH] |Gene Expression Regulation, Neoplastic [MESH] |Humans [MESH] |Liver Neoplasms/*genetics/pathology [MESH] |Prognosis [MESH] |Protein Interaction Maps/*genetics [MESH]A network biology approach to discover the molecular biomarker associa(epmc).pdf DeepDyve Pubget Overpricing