中文生物医学文献信息数据库分析

本文从生物医学数据库用户角度,浅析了中文生物医学文献信息服务的特点和数据库建库的一些要求。     

中文生物医学文献信息数据库分析

本文从生物医学数据库用户角度,浅析了中文生物医学文献信息服务的特点和数据库建库的一些要求。     

全国中文核心期刊 中国农业核心期刊

<正>《生物技术通报》是中国农业部主管、中国农业科学院农业信息研究所主办的综合性学术期刊。是美国化学文摘(CA)、中国生物医学文献服务系统(SinoMed)中的《中国生物医学文献数据库》(CBM)、中国科学引文数据库(CSCD)、中国核心期刊遴选数据库     

全国中文核心期刊 中国农业核心期刊

<正>《生物技术通报》是中国农业部主管、中国农业科学院农业信息研究所主办的学术期刊。被美国化学文摘(CA)、中国生物医学文献服务系统(SinoMed)中的《中国生物医学文献数据库》(CBM)、中国科学引文数据库(CSCD)、中国科技论文与引文数据库(CSTPCD)、中国核心期刊遴选数据库、中国学术期刊综合评价数据库等国内外重要数据库收录。入选全国中文核心期刊,中国农业核心期刊,RCCSE中国核心学术期刊(扩展版)。     

网络生物医学信息检索问题研究

随着网络技术的发展和网络信息资源的呈指教增长,网络生物医学信息的检索课题出现了一些新的问题,这些问题引起了研究人员广泛的关注.本文了介绍因特网医学信息资源的分布情况、主要类型及一些主要生物医学搜索引擎和网站及数据库的特点,对医学信息的检索途径和方法进行说明,提出了因特网医学信息检索的方法和技巧.     

全国中文核心期刊 中国农业核心期刊

<正>《生物技术通报》是中国农业部主管、中国农业科学院农业信息研究所主办的学术期刊。被美国化学文摘(CA)、中国生物医学文献服务系统(SinoMed)中的《中国生物医学文献数据库》(CBM)、中国科学引文数据库(CSCD)、中国科技论文与引文数据库(CSTPCD)、中国核心期刊遴选数据库、中国学术期刊综合评价数据库等国内外重要数据库收录。入选全国中文核心期刊,中国农业核心期刊,RCCSE中国核心学术期刊(扩展版)。     

新版STN~?为知识产权专业人员提供核心生物医学信息及专利内容

<正>美国化学文摘社和德国卡尔斯鲁厄专业情报中心近日宣布,新版STN检索平台正式上线。新版本中的数据库内容,包括生物医学数据库BIOSIS、MEDLINE和EmbaseTM,互补性的生命科学数据库CABA和FSTA,以及比Classic STN覆盖面更广泛的全球专利全文数据库,因而可为检索者提供更多所需信息。美国化学文摘社市场营销副总裁Christine McCue介绍,新版STN是一套现代高效的科研解决方案。新增的生物医学和专利数据库将会使更     

《生物技术通报》

<正>全国中文核心期刊中国农业核心期刊《生物技术通报》是中国农业部主管、中国农业科学院农业信息研究所主办的学术期刊。被美国化学文摘(CA)、中国生物医学文献服务系统(SinoMed)中的《中国生物医学文献数据库》(CBM)、中国科学引文数据库(CSCD)、中国科技论文与引文数据库(CSTPCD)、中国核心期刊遴选数据库、中国学术期刊综合评价数据库等国内外重要数据库收录。入选全国中文核心期刊,中国农业核心期刊,RCCSE中国核心学术期刊(扩展版)。     

生物数据库专栏导读北大核心CSCD

近年来,随着生物科技和信息科技的迅猛发展,基于生命组学的大数据积累和应用均已达到了前所未有的程度。多元的生物数据库资源,特别是急剧增长的生物学大数据在生物化学与分子生物学研究中得到了广泛应用,对生物学、生物医学的基础研究和转化应用起到了助推器的作用。但是,由于专业的局限性和研究者的着重点不同,部分从事生物学和生物医学的研究者对数据库资源的了解,对大数据的分析、处理和整合能力存在不足,制约了他们对已有数据资源的充分和有效的利用。为此,本期专栏选择了近年来的一些热门研究领域,特邀长期活跃在相关研究领域的一线科研工作者,结合他们自己的相关工作,分别就抗体信息数据库、细胞外囊泡数据库、蛋白质相互作用数据库、细菌sRNA数据库和RNA二级结构数据库的现状、特点和应用进行了较为系统的综述。下面对各篇文章内容给予简单介绍。     

全国中文核心期刊 中国农业核心期刊

<正>《生物技术通报》是中国农业部主管、中国农业科学院农业信息研究所主办的学术期刊。被美国化学文摘(CA)、中国生物医学文献服务系统(SinoMed)中的《中国生物医学文献数据库》(CBM)、中国科学引文数据库(CSCD)、中国科技论文与引文数据库(CSTPCD)、中国核心期刊遴选数据库、中国学术期刊综合评价数据库等国内外重要数据库收录。入选全国中文核心期刊,中国农业核心期刊,RCCSE中国核心学术期刊(扩展版)。本刊报道国内外生物技术领域基础研究成果及其在农、林、牧、渔及医     

A web service for biomedical term look-up

Recent years have seen a huge increase in the amount of biomedical information that is available in electronic format. Consequently, for biomedical researchers wishing to relate their experimental results to relevant data lurking somewhere within this expanding universe of on-line information, the ability to access and navigate biomedical information sources in an efficient manner has become increasingly important. Natural language and text processing techniques can facilitate this task by making the information contained in textual resources such as MEDLINE more readily accessible and amenable to computational processing. Names of biological entities such as genes and proteins provide critical links between different biomedical information sources and researchers' experimental data. Therefore, automatic identification and classification of these terms in text is an essential capability of any natural language processing system aimed at managing the wealth of biomedical information that is available electronically. To support term recognition in the biomedical domain, we have developed Termino, a large-scale terminological resource for text processing applications, which has two main components: first, a database into which very large numbers of terms can be loaded from resources such as UMLS, and stored together with various kinds of relevant information; second, a finite state recognizer, for fast and efficient identification and mark-up of terms within text. Since many biomedical applications require this functionality, we have made Termino available to the community as a web service, which allows for its integration into larger applications as a remotely located component, accessed through a standardized interface over the web.     

An upper-level ontology for the biomedical domain

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.     

Biomedical databases: protecting privacy and promoting research

When combined with medical information, large electronic databases of information that identify individuals provide superlative resources for genetic, epidemiology and other biomedical research. Such research resources increasingly need to balance the protection of privacy and confidentiality with the promotion of research. Models that do not allow the use of such individual-identifying information constrain research; models that involve commercial interests raise concerns about what type of access is acceptable. Researchers, individuals representing the public interest and those developing regulatory guidelines must be involved in an ongoing dialogue to identify practical models.     

Application of automatic mutation-gene pair extraction to diseases

To have a better understanding of the mechanisms of disease development, knowledge of mutations and the genes on which the mutations occur is of crucial importance. Information on disease-related mutations can be accessed through public databases or biomedical literature sources. However, information retrieval from such resources can be problematic because of two reasons: manually created databases are usually incomplete and not up to date, and reading through a vast amount of publicly available biomedical documents is very time-consuming. In this paper, we describe an automated system, MuGeX (Mutation Gene eXtractor), that automatically extracts mutation-gene pairs from Medline abstracts for a disease query. Our system is tested on a corpus that consists of 231 Medline abstracts. While recall for mutation detection alone is 85.9%, precision is 95.9%. For extraction of mutation-gene pairs, we focus on Alzheimer's disease. The recall for mutation-gene pair identification is estimated at 91.3%, and precision is estimated at 88.9%. With automatic extraction techniques, MuGeX overcomes the problems of information retrieval from public resources and reduces the time required to access relevant information, while preserving the accuracy of retrieved information.     

Digital reviews in molecular biology: approaches to structured digital publication

MOTIVATION: It is widely appreciated that it is no longer possible for biomedical research scientists to keep up with as much of what is published in their field as they ought. One solution to this problem is to increase the efficiency of information use by moving away from the classical browsing model for scientific information dissemination towards an information on demand model which would allow researchers to access information quickly and efficiently only as they need it. The most common approach to this goal has been to use information retrieval technology to improve access to text databases of biomedical information. We are interested in exploring an alternative; encoding this information for storage in structured databases for efficient retrieval. RESULTS: Two small databases described here are test beds for development of structured digital publication software; the Tumor Gene Database, containing information about genes which are the sites for cancer-causing mutations, and the Mammary Transgene Database, containing information about expression of transgenes in agriculturally important animals. Both have been successfully searched by users and edited by curators via the World Wide Web.     

The landscape for epigenetic/epigenomic biomedical resources

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics.     

The landscape for epigenetic/epigenomic biomedical resources

Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Efficient Generation of Germ Line Transmitting Chimeras from C57BL/6N ES Cells by Aggregation with Outbred Host Embryos

Genetically modified mouse strains derived from embryonic stem (ES) cells have become essential tools for functional genomics and biomedical research. Large scale mutagenesis projects are producing libraries of mutant C57BL/6 (B6) ES cells to enable the functional annotation of every gene of the mouse genome. To realize the utility of these resources, efficient and accessible methods of generating mutant mice from these ES cells are necessary. Here, we describe a combination of ICR morula aggregation and a chemically-defined culture medium with widely available and accessible components for the high efficiency generation of germline transmitting chimeras from C57BL/6N ES cells. Together these methods will ease the access of the broader biomedical research community to the publicly available B6 ES cell resources.