<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christopher J.O. Baker</style></author><author><style face="normal" font="default" size="100%">René Witte</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enriching Protein Structure Visualizations with Mutation Annotations Obtained by Text Mining Protein Engineering Literature</style></title><secondary-title><style face="normal" font="default" size="100%">The 3rd Canadian Working Conference on Computational Biology (CCCB'04)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ProSAT</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Function</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Structure Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Xylanase</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">October 4</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www-927.ibm.com/ibm/cas/publications/TR-74.203/8/TR-74-203-8.pdf</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">http://www.semanticsoftware.info/system/files/baker_witte_cccb04.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Markham, Ontario, Canada</style></pub-location><abstract><style face="normal" font="default" size="100%">Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is not easily integrated and must be uploaded from databases or provided through manual curation of input files. We describe a mixed natural language processing and sequence analysis based approach for the retrieval of mutation specific annotations from full text articles for rendering with protein structures.</style></abstract><notes><style face="normal" font="default" size="100%">Co-located with IBM CASCON.</style></notes><custom1><style face="normal" font="default" size="100%">Copyright © 2004 Christopher Baker and René Witte.</style></custom1></record></records></xml>
