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10.1186/s13742-015-0084-3 http://scihub22266oqcxt.onion/10.1186/s13742-015-0084-3 C4570295!4570295!26380076     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Gigascience 2015 ; 4 (ä): ä Nephropedia Template TP {{tp|p=26380076|t=2015. Metabolome of human gut microbiome is predictive of host dysbiosis |pdf=|usr=}}{{26380076}} gab.com Text Metabolome of human gut microbiome is predictive of host dysbiosis JO: Gigascience http://www.kidney.de/pget.php?&tw=1&pmid=26380076 #FOAvip Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome?s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome?host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions. Electronic supplementary material: The online version of this article (doi:10.1186/s13742-015-0084-3) contains supplementary material, which is available to authorized users. Twit Text FOAVip Metabolome of human gut microbiome is predictive of host dysbiosis ????Gigascience http://www.kidney.de/pget.php?&tw=1&pmid=26380076 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26380076 #FOAvip English Wikipedia <ref>{{Cite pmid|26380076}}</ref> Metabolome of human gut microbiome is predictive of host dysbiosis #MMPMID26380076 Larsen PE; Dai Y Gigascience 2015[]; 4 (ä): ä PMID26380076show ga Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome?s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome?host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions. Electronic supplementary material: The online version of this article (doi:10.1186/s13742-015-0084-3) contains supplementary material, which is available to authorized users. äMetabolome of human gut microbiome is predictive of host dysbiosis (epmc).pdf DeepDyve Pubget Overpricing