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10.1038/s41598-017-15231-w http://scihub22266oqcxt.onion/10.1038/s41598-017-15231-w C5673929!5673929 !29109515     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29109515 &cmd=llinks): Failed to open stream: HTTP request failed! 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Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets |pdf=|usr=}}{{29109515 }} gab.com Text Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29109515 #FOAvip Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH's efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu. Twit Text FOAVip Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29109515 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29109515 #FOAvip English Wikipedia <ref>{{Cite pmid|29109515 }}</ref> Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets #MMPMID29109515 Barupal DK ; Fiehn O Sci Rep 2017[Nov]; 7 (1 ): 14567 PMID29109515 show ga Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH's efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu. |*Data Interpretation, Statistical [MESH] |*Metabolic Networks and Pathways [MESH] |*Metabolomics/methods [MESH] |Animals [MESH] |Biological Ontologies [MESH] |Datasets as Topic [MESH] |Diabetes Mellitus, Experimental/etiology/metabolism [MESH] |Disease Models, Animal [MESH] |Humans [MESH]Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to b(epmc).pdf DeepDyve Pubget Overpricing