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At least 145 records · Page 8Linked to original sources
PubMed2004

PeerGAD: a peer-review-based and community-centric web application for viewing and annotating prokaryotic genome sequences.

PeerGAD is a web-based database-driven application that allows community-wide peer-reviewed annotation of prokaryotic genome sequences. The application was developed to support the annotation of the Pseudomonas syringae pv. tomato strain DC3000 genome sequence and is easily portable to other genome sequence annotation projects. PeerGAD incorporates several innovative design and operation features and accepts annotations pertaining to gene naming, role classification, gene translation and annotation derivation. The annotator tool in PeerGAD is built around a genome browser that offers users the ability to search and navigate the genome sequence. Because the application encourages annotation of the genome sequence directly by researchers and relies on peer review, it circumvents the need for an annotation curator while providing added value to the annotation data. Support for the Gene Ontology vocabulary, a structured and controlled vocabulary used in classification of gene roles, is emphasized throughout the system. Here we present the underlying concepts integral to the functionality of PeerGAD.

Computational Biology↗
PubMed2000

Discovering missed synonymy in a large concept-oriented Metathesaurus.

The Unified Medical Language System (UMLS) [1, 2] Metathesuarus is concept-oriented; its goal is to unite all names with identical meaning in a single Concept. The names come from its constituent vocabularies or "sources"--a wide variety of biomedical terminologies including many controlled vocabularies and classifications used in patient records, administrative health data, bibliographic, research, full-text, and expert systems. Many offer little definitional information, and many are not themselves concept-oriented, so identifying synonymy is a challenging semantic task [3]. The rapidly increasing size of the Metathesaurus makes the task daunting, demanding effective computational support; there are more than 1.5 million names for 730,000 concepts in the January 2000 release. Vocabularies are added and updated using sophisticated lexical matching, selective algorithms, and expert review [4, 5, 6]. Yet the result is imperfect; we have discovered and corrected missed synonymy in approximately 1% of previously released concepts each year. This paper reviews general methods for finding missed synonymy and describes several specific novel approaches which we have found effective.

Algorithms↗
PubMed2005

PHSkb: a knowledgebase to support notifiable disease surveillance.

BACKGROUND: Notifiable disease surveillance in the United States is predominantly a passive process that is often limited by poor timeliness and low sensitivity. Interoperable tools are needed that interact more seamlessly with existing clinical and laboratory data to improve notifiable disease surveillance. DESCRIPTION: The Public Health Surveillance Knowledgebase (PHSkb) is a computer database designed to provide quick, easy access to domain knowledge regarding notifiable diseases and conditions in the United States. The database was developed using Protégé ontology and knowledgebase editing software. Data regarding the notifiable disease domain were collected via a comprehensive review of state health department websites and integrated with other information used to support the National Notifiable Diseases Surveillance System (NNDSS). Domain concepts were harmonized, wherever possible, to existing vocabulary standards. The knowledgebase can be used: 1) as the basis for a controlled vocabulary of reportable conditions needed for data aggregation in public health surveillance systems; 2) to provide queriable domain knowledge for public health surveillance partners; 3) to facilitate more automated case detection and surveillance decision support as a reusable component in an architecture for intelligent clinical, laboratory, and public health surveillance information systems. CONCLUSIONS: The PHSkb provides an extensible, interoperable system architecture component to support notifiable disease surveillance. Further development and testing of this resource is needed.

Communicable Diseases↗
PubMed2006

[caCORE: core architecture of bioinformation on cancer research in America].

A critical factor in the advancement of biomedical research is the ease with which data can be integrated, redistributed and analyzed both within and across domains. This paper summarizes the Biomedical Information Core Infrastructure built by National Cancer Institute Center for Bioinformatics in America (NCICB). The main product from the Core Infrastructure is caCORE--cancer Common Ontologic Reference Environment, which is the infrastructure backbone supporting data management and application development at NCICB. The paper explains the structure and function of caCORE: (1) Enterprise Vocabulary Services (EVS). They provide controlled vocabulary, dictionary and thesaurus services, and EVS produces the NCI Thesaurus and the NCI Metathesaurus; (2) The Cancer Data Standards Repository (caDSR). It provides a metadata registry for common data elements. (3) Cancer Bioinformatics Infrastructure Objects (caBIO). They provide Java, Simple Object Access Protocol and HTTP-XML application programming interfaces. The vision for caCORE is to provide a common data management framework that will support the consistency, clarity, and comparability of biomedical research data and information. In addition to providing facilities for data management and redistribution, caCORE helps solve problems of data integration. All NCICB-developed caCORE components are distributed under open-source licenses that support unrestricted usage by both non-profit and commercial entities, and caCORE has laid the foundation for a number of scientific and clinical applications. Based on it, the paper expounds caCORE-base applications simply in several NCI projects, of which one is CMAP (Cancer Molecular Analysis Project), and the other is caBIG (Cancer Biomedical Informatics Grid). In the end, the paper also gives good prospects of caCORE, and while caCORE was born out of the needs of the cancer research community, it is intended to serve as a general resource. Cancer research has historically contributed to many areas beyond tumor biology. At the same time, the paper makes some suggestions about the study at the present time on biomedical informatics in China.

Computational Biology↗
PubMed2004

[Italian Thesaurus of Bioethics, TIB].

The article aims at illustrating the characteristics and functions of a monolingual thesaurus, focusing on the Italian Thesaurus of Bioethics (Thesaurus Italiano di Bioetica, TIB) the controlled vocabulary used to index and retrieve documents within SIBIL (Italian Online Bioethics Information System). TIB includes controlled terms (descriptors) translated from the Bioethics Thesaurus adopted by the Kennedy Institute of Ethics of the Georgetown University of Washington and revised according to the Italian context of study and scientific debate in the field of bioethics. The overall amount of TIB terms consists in over 1600 headings. Methods to link thesaurus terms hierarchically, by association and by showing synonyms as recommended in ISO standards are applied with reference to descriptors drawn from TIB. Future plans to make the English version of TIB available online within European networks are also illustrated, aiming at spreading information relating to bioethics at an international level.

Bioethical Issues↗
PubMed1997

Maintaining the integrity of a Web-based medical vocabulary glossary.

Medical vocabulary databases are vital components of electronic medical record (EMR) systems. While their performance and efficiency has been extensively explored by many authors, few have dealt with the maintenance of their semantic integrity. Clinicians' preference for an optimistic system has introduced a need for monitoring and filtering proposed additions to the vocabulary tables. We propose the use of batch processing and memo-posting as means of implementing a World-Wide-Web-based Controlled Vocabulary Glossary as a monitored optimistic system.

Computer Communication Networks↗
PubMed1995

Managing vocabulary for a centralized clinical system.

The clinical computing environment at Columbia-Presbyterian Medical Center is organized around a centralized database of coded patient information collected from various ancillary sources. The Medical Entities Dictionary (MED) is the central repository for the controlled vocabulary used to encode the patient data. The MED is composed of terms used in the ancillary departments and, as such, changes in the source vocabularies must be maintained in the MED. The MED also contains some basic knowledge about the terms, and sophisticated maintenance tools have been developed that take advantage of this knowledge. This paper describes the success of the knowledge-based approach by describing the techniques used in two tasks: addition of a new vocabulary and maintenance of an existing one.

Clinical Laboratory Information Systems↗
PubMed1991

An interlingua for electronic interchange of medical information: using frames to map between clinical vocabularies.

The proliferation of medical knowledge has led to the development of extensive dictionaries for electronically accessing information resources. The task of standardizing terminology used for electronic hospital records and for knowledge bases for medical expert systems and indexing the medical literature cannot easily be met by developing a single, monolithic "official" medical vocabulary. Developing a monolithic vocabulary would require a massive effort, and its existence would not guarantee its use by third-party payors, by practicing clinicians, or by developers of electronic medical information systems. Recognizing this, the National Library of Medicine (NLM) has begun to develop the Unified Medical Language System (UMLS) as a means of promoting electronic information exchange among systems with controlled vocabularies. The authors describe a frame-based system developed as an experimental approach to mapping between controlled clinical vocabularies.

Cluster Analysis↗
PubMed2006

The Gene Ontology (GO) project in 2006.

The Gene Ontology (GO) project (http://www.geneontology.org) develops and uses a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://song.sourceforge.net/). The GO Consortium continues to improve to the vocabulary content, reflecting the impact of several novel mechanisms of incorporating community input. A growing number of model organism databases and genome annotation groups contribute annotation sets using GO terms to GO's public repository. Updates to the AmiGO browser have improved access to contributed genome annotations. As the GO project continues to grow, the use of the GO vocabularies is becoming more varied as well as more widespread. The GO project provides an ontological annotation system that enables biologists to infer knowledge from large amounts of data.

Database Management Systems↗
PubMed1996

Inter-rater reliability and review of the VA unresolved narratives.

To better understand how VA clinicians use medical vocabulary in every day practice, we set out to characterize terms generated in the Problem List module of the VA's DHCP system that were not mapped to terms in the controlled-vocabulary lexicon of DHCP. When entered terms fail to match those in the lexicon, a note is sent to a central repository. When our study started, the volume in that repository had reached 16,783 terms. We wished to characterize the potential reasons why these terms failed to match terms in the lexicon. After examining two small samples of randomly selected terms, we used group consensus to develop a set of rating criteria and a rating form. To be sure that the results of multiple reviewers could be confidently compared, we analyzed the inter-rater agreement of our rating process. Two rates used this form to rate the same 400 terms. We found that modifiers and numeric data were common and consistent reasons for failure to match, while others such as use of synonyms and absence of the concept from the lexicon were common but less consistently selected.

Medical Records Systems, Computerized↗
PubMed2006

IntAct--open source resource for molecular interaction data.

IntAct is an open source database and software suite for modeling, storing and analyzing molecular interaction data. The data available in the database originates entirely from published literature and is manually annotated by expert biologists to a high level of detail, including experimental methods, conditions and interacting domains. The database features over 126,000 binary interactions extracted from over 2100 scientific publications and makes extensive use of controlled vocabularies. The web site provides tools allowing users to search, visualize and download data from the repository. IntAct supports and encourages local installations as well as direct data submission and curation collaborations. IntAct source code and data are freely available from http://www.ebi.ac.uk/intact.

DNA↗
PubMed2003

VUMeF: extending the French involvement in the UMLS Metathesaurus.

A considerable number of robust vocabularies and thesauri have been developed for the healthcare and biomedical domain. No single vocabulary, however; provides complete coverage of the information needs from a public health perspective. The results of an investigation of vocabulary sources for the development of a comprehensive controlled vocabulary for the public health domain at the Centers for Disease Control and Prevention (CDC) is presented.

France↗
PubMed1999

Ontology for immunogenetics: the IMGT-ONTOLOGY.

MOTIVATION: IMGT, the international ImMunoGeneTics database (http:@imgt.cines.fr:8104), created by M.-P. Lefranc, is an integrated database specializing in antigen receptors (immunoglobulins and T-cell receptors) and major histocompatibility complex (MHC) of all vertebrate species. IMGT accurate immunogenetics data are based on the standardization of the biological knowledge provided by the 'ImMunoGeneTics' IMGT-ONTOLOGY. The IMGT-ONTOLOGY describes the classification and specification of terms needed for immunogenetics and bioinformatics. IMGT-ONTOLOGY covers four main concepts: 'IDENTIFICATION', 'DESCRIPTION', 'CLASSIFICATION' and 'OBTENTION'. These concepts allow an extensive and standardized description and characterization of immunoglobulin and T-cell receptor data. The controlled vocabulary and the annotation rules are indispensable to ensure accuracy, consistency and coherence in IMGT. IMGT-ONTOLOGY allows scientists and clinicians to use, for the first time, identical terms with the same meaning in immunogenetics. It provides a semantic repository that will improve interoperability between specialist and generalist databases.

Animals↗
PubMed2006

PubFocus: semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm.

BACKGROUND: Understanding research activity within any given biomedical field is important. Search outputs generated by MEDLINE/PubMed are not well classified and require lengthy manual citation analysis. Automation of citation analytics can be very useful and timesaving for both novices and experts. RESULTS: PubFocus web server automates analysis of MEDLINE/PubMed search queries by enriching them with two widely used human factor-based bibliometric indicators of publication quality: journal impact factor and volume of forward references. In addition to providing basic volumetric statistics, PubFocus also prioritizes citations and evaluates authors' impact on the field of search. PubFocus also analyses presence and occurrence of biomedical key terms within citations by utilizing controlled vocabularies. CONCLUSION: We have developed citations' prioritisation algorithm based on journal impact factor, forward referencing volume, referencing dynamics, and author's contribution level. It can be applied either to the primary set of PubMed search results or to the subsets of these results identified through key terms from controlled biomedical vocabularies and ontologies. NCI (National Cancer Institute) thesaurus and MGD (Mouse Genome Database) mammalian gene orthology have been implemented for key terms analytics. PubFocus provides a scalable platform for the integration of multiple available ontology databases. PubFocus analytics can be adapted for input sources of biomedical citations other than PubMed.

Algorithms↗
PubMed1996

Population groups: indexing, coverage, and retrieval effectiveness of ethnically related health care issues in health sciences databases.

OBJECTIVES: This study examined methods of accessing (for indexing and retrieval purposes) medical research on population groups in the major abstracting and indexing services of the health sciences literature. DESIGN: The study of diseases in specific population groups is facilitated by the indexing of both diseases and populations in a database. The MEDLINE, PsycINFO, and Embase databases were selected for the study. The published thesauri for these databases were examined to establish the vocabulary in use. Indexing terms were identified and examined as to their representation in the current literature. Terms were clustered further into groups thought to reflect an end user's perspective and to facilitate subsequent analysis. The medical literature contained in the three online databases was searched with both controlled vocabulary and natural language terms. RESULTS: The three thesauri revealed shallow pre-coordinated hierarchical structures, rather difficult-to-use terms for post-coordination, and a blurring of cultural, genetic, and racial facets of populations. Post-coordination is difficult because of the system-oriented terminology, which is intended mostly for information professionals. The terminology unintentionally restricts access by the end users who lack the knowledge needed to use the thesauri effectively for information retrieval. CONCLUSIONS: Population groups are not represented adequately in the index languages of health sciences databases. Users of these databases need to be alerted to the difficulties that may be encountered in searching for information on population groups. Information and health professionals may not be able to access the literature if they are not familiar with the indexing policies on population groups. Consequently, the study points to a problem that needs to be addressed, through either the redesign of existing systems or the design of new ones to meet the goals of Healthy People 2000 and beyond.

Abstracting and Indexing↗
PubMed1998

MHCWeb: converting a WWW database into a knowledge-based collaborative environment.

The World Wide Web (WWW) is useful for distributing scientific data. Most existing web data resources organize their information either in structured flat files or relational databases with basic retrieval capabilities. For databases with one or a few simple relations, these approaches are successful, but they can be cumbersome when there is a data model involving multiple relations between complex data. We believe that knowledge-based resources offer a solution in these cases. Knowledge bases have explicit declarations of the concepts in the domain, along with the relations between them. They are usually organized hierarchically, and provide a global data model with a controlled vocabulary. We have created the OWEB architecture for building online scientific data resources using knowledge bases. OWEB provides a shell for structuring data, providing secure and shared access, and creating computational modules for processing and displaying data. In this paper, we describe the translation of the online immunological database MHCPEP into an OWEB system called MHCWeb. This effort involved building a conceptual model for the data, creating a controlled terminology for the legal values for different types of data, and then translating the original data into the new structure. The OWEB environment allows for flexible access to the data by both users and computer programs.

Artificial Intelligence↗