{"title":"生物医学文献中蛋白质关联的发现","authors":"Yueyu Fu, Javed Mostafa, Kazuhiro Seki","doi":"10.1109/JCDL.2003.1204848","DOIUrl":null,"url":null,"abstract":"Protein association discovery can directly contribute toward developing protein pathways; hence it is a significant problem in bioinformatics. LUCAS (Library of User-Oriented Concepts for Access Services) was designed to automatically extract and determine associations among proteins from biomedical literature. Such a tool has notable potential to automate database construction in biomedicine, instead of relying on experts' analysis. We report on the mechanisms for automatically generating clusters of proteins. A formal evaluation of the system, based on a subset of 2000 MEDLINE titles and abstracts, has been conducted against Swiss-Prot database in which the associations among concepts are entered by experts manually.","PeriodicalId":248854,"journal":{"name":"2003 Joint Conference on Digital Libraries, 2003. Proceedings.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Protein association discovery in biomedical literature\",\"authors\":\"Yueyu Fu, Javed Mostafa, Kazuhiro Seki\",\"doi\":\"10.1109/JCDL.2003.1204848\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Protein association discovery can directly contribute toward developing protein pathways; hence it is a significant problem in bioinformatics. LUCAS (Library of User-Oriented Concepts for Access Services) was designed to automatically extract and determine associations among proteins from biomedical literature. Such a tool has notable potential to automate database construction in biomedicine, instead of relying on experts' analysis. We report on the mechanisms for automatically generating clusters of proteins. A formal evaluation of the system, based on a subset of 2000 MEDLINE titles and abstracts, has been conducted against Swiss-Prot database in which the associations among concepts are entered by experts manually.\",\"PeriodicalId\":248854,\"journal\":{\"name\":\"2003 Joint Conference on Digital Libraries, 2003. Proceedings.\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 Joint Conference on Digital Libraries, 2003. Proceedings.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/JCDL.2003.1204848\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 Joint Conference on Digital Libraries, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/JCDL.2003.1204848","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Protein association discovery in biomedical literature
Protein association discovery can directly contribute toward developing protein pathways; hence it is a significant problem in bioinformatics. LUCAS (Library of User-Oriented Concepts for Access Services) was designed to automatically extract and determine associations among proteins from biomedical literature. Such a tool has notable potential to automate database construction in biomedicine, instead of relying on experts' analysis. We report on the mechanisms for automatically generating clusters of proteins. A formal evaluation of the system, based on a subset of 2000 MEDLINE titles and abstracts, has been conducted against Swiss-Prot database in which the associations among concepts are entered by experts manually.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
