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10.1002/btpr.2230 http://scihub22266oqcxt.onion/10.1002/btpr.2230 C5289651!5289651 !26785356     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26785356 &cmd=llinks): Failed to open stream: HTTP request failed! 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Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs |pdf=|usr=}}{{26785356 }} gab.com Text Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs JO: Biotechnol Prog http://www.kidney.de/pget.php?&tw=1&pmid=26785356 #FOAvip Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. Twit Text FOAVip Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs ????Biotechnol Prog http://www.kidney.de/pget.php?&tw=1&pmid=26785356 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26785356 #FOAvip English Wikipedia <ref>{{Cite pmid|26785356 }}</ref> Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs #MMPMID26785356 Kaiser JL ; Bland CL ; Klinke DJ 2nd Biotechnol Prog 2016[Mar]; 32 (2 ): 470-9 PMID26785356 show ga Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. |*Bayes Theorem [MESH] |Antineoplastic Agents/chemistry/*pharmacology [MESH] |Breast Neoplasms/*drug therapy/*immunology/pathology [MESH] |Cell Proliferation/drug effects [MESH] |Epithelial-Mesenchymal Transition/drug effects [MESH] |Female [MESH] |Humans [MESH] |Immunity [MESH]Identifying causal networks linking cancer processes and anti-tumor im(epmc).pdf DeepDyve Pubget Overpricing