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10.1038/nbt0210-128 http://scihub22266oqcxt.onion/10.1038/nbt0210-128 C4829499!4829499 !20139945     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=20139945 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\20139945 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nat+Biotechnol 2010 ; 28 (2 ): 128-30 Nephropedia Template TP {{tp|p=20139945 |t=2010. Ontology engineering |pdf=|usr=}}{{20139945 }} gab.com Text Ontology engineering JO: Nat Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=20139945 #FOAvip Gene Ontology and similar biomedical ontologies are critical tools of today genetic research. These ontologies are crafted through a painstaking process of manual editing, and their organization relies on the intuition of human curators. Here we describe a method that uses information theory to automatically organize the structure of GO and optimize the distribution of the information within it. We used this approach to analyze the evolution of GO, and we identified several areas where the information was suboptimally organized. We optimized the structure of GO and used it to analyze 10,117 gene expression signatures. The use of this new version changed the functional interpretations of 97.5% (p < 10-3) of the signatures by, on average, 14.6%. As a result of this analysis, several changes will be introduced in the next releases of GO. We expect that these formal methods will become the standard to engineer biomedical ontologies. Twit Text FOAVip Ontology engineering ????Nat Biotechnol http://www.kidney.de/pget.php?&tw=1&pmid=20139945 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=20139945 #FOAvip English Wikipedia <ref>{{Cite pmid|20139945 }}</ref> Ontology engineering #MMPMID20139945 Alterovitz G ; Xiang M ; Hill DP ; Lomax J ; Liu J ; Cherkassky M ; Dreyfuss J ; Mungall C ; Harris MA ; Dolan ME ; Blake JA ; Ramoni MF Nat Biotechnol 2010[Feb]; 28 (2 ): 128-30 PMID20139945 show ga Gene Ontology and similar biomedical ontologies are critical tools of today genetic research. These ontologies are crafted through a painstaking process of manual editing, and their organization relies on the intuition of human curators. Here we describe a method that uses information theory to automatically organize the structure of GO and optimize the distribution of the information within it. We used this approach to analyze the evolution of GO, and we identified several areas where the information was suboptimally organized. We optimized the structure of GO and used it to analyze 10,117 gene expression signatures. The use of this new version changed the functional interpretations of 97.5% (p < 10-3) of the signatures by, on average, 14.6%. As a result of this analysis, several changes will be introduced in the next releases of GO. We expect that these formal methods will become the standard to engineer biomedical ontologies. |*Terminology as Topic [MESH] |Computational Biology/*methods [MESH] |Data Mining/*methods [MESH] |Genes/*genetics [MESH] |Genetic Engineering/*statistics & numerical data [MESH]Ontology engineering (epmc).pdf DeepDyve Pubget Overpricing