{"title":"从生物医学文献中挖掘基因信息","authors":"Catalina O. Tudor, K. Vijay-Shanker, C. Schmidt","doi":"10.3115/1572306.1572311","DOIUrl":null,"url":null,"abstract":"eGIFT (Extracting Gene Information From Text) is an intelligent system which is intended to aid scientists in surveying literature relevant to genes of interest. From a gene specific set of abstracts retrieved from PubMed, eGIFT determines the most important terms associated with the given gene. Annotators using eGIFT can quickly find articles describing gene functions and individuals scientists surveying the results of high-throughput experiments can quickly extract information important to their hits.","PeriodicalId":200974,"journal":{"name":"Workshop on Biomedical Natural Language Processing","volume":"405 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Mining the Biomedical Literature for Genic Information\",\"authors\":\"Catalina O. Tudor, K. Vijay-Shanker, C. Schmidt\",\"doi\":\"10.3115/1572306.1572311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"eGIFT (Extracting Gene Information From Text) is an intelligent system which is intended to aid scientists in surveying literature relevant to genes of interest. From a gene specific set of abstracts retrieved from PubMed, eGIFT determines the most important terms associated with the given gene. Annotators using eGIFT can quickly find articles describing gene functions and individuals scientists surveying the results of high-throughput experiments can quickly extract information important to their hits.\",\"PeriodicalId\":200974,\"journal\":{\"name\":\"Workshop on Biomedical Natural Language Processing\",\"volume\":\"405 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Biomedical Natural Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/1572306.1572311\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Biomedical Natural Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/1572306.1572311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining the Biomedical Literature for Genic Information
eGIFT (Extracting Gene Information From Text) is an intelligent system which is intended to aid scientists in surveying literature relevant to genes of interest. From a gene specific set of abstracts retrieved from PubMed, eGIFT determines the most important terms associated with the given gene. Annotators using eGIFT can quickly find articles describing gene functions and individuals scientists surveying the results of high-throughput experiments can quickly extract information important to their hits.
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