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10.1016/j.jbi.2017.06.002 http://scihub22266oqcxt.onion/10.1016/j.jbi.2017.06.002 C5602605!5602605 !28600026     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28600026 &cmd=llinks): Failed to open stream: HTTP request failed! 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MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis |pdf=|usr=}}{{28600026 }} gab.com Text MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=28600026 #FOAvip Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. CASE: edu/MetabolitePredict. Twit Text FOAVip MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=28600026 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28600026 #FOAvip English Wikipedia <ref>{{Cite pmid|28600026 }}</ref> MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis #MMPMID28600026 Wang Q ; Xu R J Biomed Inform 2017[Jul]; 71 (ä): 222-228 PMID28600026 show ga Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. CASE: edu/MetabolitePredict. |*Algorithms [MESH] |*Arthritis, Rheumatoid [MESH] |*Metabolomics [MESH] |Biomarkers [MESH] |Forecasting [MESH]MetabolitePredict: A de novo human metabolomics prediction system and (epmc).pdf DeepDyve Pubget Overpricing