基于PubMed数据源的生物医学文献检索工具应用特点

从检索方式和检索结果处理两个方面比较12种基于PubMed数据源的医学文献检索工具的应用特点,并从改变检索界面、相关性排序和检索结果聚类3个方面总结这类检索工具的创新之处,指出Entrez/PubMed自身新的改进。     

GenBank数据库和PubMed数据库中序列数据信息检索比较

通过对GenBank数据库和PubMed数据库的数据来源、检索界面和检索结果等的对比分析,发现2个检索库检索的序列数据信息存在差别,GenBank数据库检索结果和检准率均高于PubMed数据库。     

The NIF LinkOut Broker: A Web Resource to Facilitate Federated Data Integration using NCBI Identifiers

This paper describes the NIF LinkOut Broker (NLB) that has been built as part of the Neuroscience Information Framework (NIF) project. The NLB is designed to coordinate the assembly of links to neuroscience information items (e.g., experimental data, knowledge bases, and software tools) that are (1) accessible via the Web, and (2) related to entries in the National Center for Biotechnology Information’s (NCBI’s) Entrez system. The NLB collects these links from each resource and passes them to the NCBI which incorporates them into its Entrez LinkOut service. In this way, an Entrez user looking at a specific Entrez entry can LinkOut directly to related neuroscience information. The information stored in the NLB can also be utilized in other ways. A second approach, which is operational on a pilot basis, is for the NLB Web server to create dynamically its own Web page of LinkOut links for each NCBI identifier in the NLB database. This approach can allow other resources (in addition to the NCBI Entrez) to LinkOut to related neuroscience information. The paper describes the current NLB system and discusses certain design issues that arose during its implementation.     

基于Entrez系统利用生物信息数据库

阐述生物信息数据库分类及特点,着重介绍如何使用Entrez系统充分利用生物信息数据库。指出生物信息数据库在生物信息学的发展过程中发挥了巨大作用,促进了生物信息学的发展。     

集成化数据库检索系统Entrez的开发与应用

Entrez是美国生物技术信息中心开发研制的集成化数据库检索系统,用户可以检索和浏览与基因库(GenBank)链接的所有数据库.本文主要探讨了新加入到Entrez数据检索系统的PubMed以及具有特殊功能的邻接系统(Neighboring System).     

GenBank数据库检索及其应用——Entrez检索功能

GenBank数据库是世界上著名的生物信息数据库,包含了目前所有已知的核苷酸序列和蛋白质序列以及与它们相关的文献著作和生物学注释。详细介绍了它的Entrez检索功能。     

Automated comparative auditing of NCIT genomic roles using NCBI

Biomedical research has identified many human genes and various knowledge about them. The National Cancer Institute Thesaurus (NCIT) represents such knowledge as concepts and roles (relationships). Due to the rapid advances in this field, it is to be expected that the NCIT’s Gene hierarchy will contain role errors. A comparative methodology to audit the Gene hierarchy with the use of the National Center for Biotechnology Information’s (NCBI’s) Entrez Gene database is presented. The two knowledge sources are accessed via a pair of Web crawlers to ensure up-to-date data. Our algorithms then compare the knowledge gathered from each, identify discrepancies that represent probable errors, and suggest corrective actions. The primary focus is on two kinds of gene-roles: (1) the chromosomal locations of genes, and (2) the biological processes in which genes play a role. Regarding chromosomal locations, the discrepancies revealed are striking and systematic, suggesting a structurally common origin. In regard to the biological processes, difficulties arise because genes frequently play roles in multiple processes, and processes may have many designations (such as synonymous terms). Our algorithms make use of the roles defined in the NCIT Biological Process hierarchy to uncover many probable gene-role errors in the NCIT. These results show that automated comparative auditing is a promising technique that can identify a large number of probable errors and corrections for them in a terminological genomic knowledge repository, thus facilitating its overall maintenance.     

Principles for Effective PubMed Searching

Abstract

PubMed is a window to the esteemed biomedical citation database MEDLINE, developed at the National Institutes of Health in Bethesda, Maryland. The PubMed interface has evolved into a robust system with enhanced search features and is freely available on the Internet. However, to utilize PubMed effectively, the user must understand how to take full advantage of its less than intuitive tools. Each of the PubMed Advanced Search features is described and illustrated with a sample search. Important MEDLINE syntax is explained as well. In addition, elements for best search practices are suggested. Finally, an appendix to online resources for PubMed facts and instruction is provided.