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10.1109/ICCABS.2012.6182637 http://scihub22266oqcxt.onion/10.1109/ICCABS.2012.6182637 C4593623!4593623 !26448899     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26448899 &cmd=llinks): Failed to open stream: HTTP request failed! 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The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics |pdf=|usr=}}{{26448899 }} gab.com Text The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics JO: IEEE Int Conf Comput Adv Bio Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=26448899 #FOAvip Metabolomics is a rapidly growing field studying the small-molecule metabolite profile of a biological organism. Studying metabolism has a potential to contribute to biomedical research as well as drug discovery. One of the current challenges in metabolomics is the identification of unknown metabolites as existing chemical databases are incomplete. We present a novel way of utilizing known mammalian metabolites in an effort to identify unknown ones. The system relies on a mammalian scaffolds database to aid the classification process. The results show that 96% of the mammalian compounds were identified as truly mammalian in a leave-one-out experiment. The system was also tested with a random set of synthetic compounds, downloaded from ChemBridge and ChemSynthesis databases. The system was able to eliminate 54% of the set, leaving 46% of the compounds as potentially unknown mammalian metabolites. Twit Text FOAVip The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics ????IEEE Int Conf Comput Adv Bio Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=26448899 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26448899 #FOAvip English Wikipedia <ref>{{Cite pmid|26448899 }}</ref> The use of graph matching algorithms to identify biochemical substructures in synthetic chemical compounds: Application to metabolomics #MMPMID26448899 Hamdalla M ; Grant D ; Mandoiu I ; Hill D ; Rajasekaran S ; Ammar R IEEE Int Conf Comput Adv Bio Med Sci 2012[Feb]; 2012 (ä): ä PMID26448899 show ga Metabolomics is a rapidly growing field studying the small-molecule metabolite profile of a biological organism. Studying metabolism has a potential to contribute to biomedical research as well as drug discovery. One of the current challenges in metabolomics is the identification of unknown metabolites as existing chemical databases are incomplete. We present a novel way of utilizing known mammalian metabolites in an effort to identify unknown ones. The system relies on a mammalian scaffolds database to aid the classification process. The results show that 96% of the mammalian compounds were identified as truly mammalian in a leave-one-out experiment. The system was also tested with a random set of synthetic compounds, downloaded from ChemBridge and ChemSynthesis databases. The system was able to eliminate 54% of the set, leaving 46% of the compounds as potentially unknown mammalian metabolites. äThe use of graph matching algorithms to identify biochemical substruct(epmc).pdf DeepDyve Pubget Overpricing