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Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6452
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dc.creatorLathrop, Richard H.-
dc.creatorWebster, Teresa A.-
dc.creatorSmith, Temple F.-
dc.date2004-10-04T14:56:57Z-
dc.date2004-10-04T14:56:57Z-
dc.date1987-05-01-
dc.date.accessioned2013-10-09T02:45:31Z-
dc.date.available2013-10-09T02:45:31Z-
dc.date.issued2013-10-09-
dc.identifierAIM-902-
dc.identifierhttp://hdl.handle.net/1721.1/6452-
dc.identifier.urihttp://koha.mediu.edu.my:8181/xmlui/handle/1721-
dc.descriptionThere are many situations in which a very detailed low-level description encodes, through a hierarchical organization, a recognizable higher-order pattern. The macro-molecular structural conformations of proteins exhibit higher order regularities whose recognition is complicated by many factors. ARIADNE searches for similarities between structural descriptors and hypothesized protein structure at levels more abstract than the primary sequence, based on differential similarity to rule antecedents and the controlled use of tentative higher-order structural hypotheses. Inference is grounded solely in knowledge derivable from the primary sequence, and exploits secondary structure predictions. A novel proposed alignment and functional domain identification of the aminoacyl-tRNA synthetases was found using this system.-
dc.format3199888 bytes-
dc.format1268341 bytes-
dc.formatapplication/postscript-
dc.formatapplication/pdf-
dc.languageen_US-
dc.relationAIM-902-
dc.titleARIADNE: Pattern-Directed Inference and Hierarchical Abstraction in Protein Structure Recognition-
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