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on page 1 showing 10 out of 10 results from 1 sources

Cite this (Genetic Association Database, RRID:SCR_013264)

URL: http://geneticassociationdb.nih.gov/

Resource Type: Resource, data or information resource, database

The Genetic Association Database is an archive of human genetic association studies of complex diseases and disorders. The goal of this database is to allow the user to rapidly identify medically relevant polymorphism from the large volume of polymorphism and mutational data, in the context of standardized nomenclature. The data is from published scientific papers. Study data is recorded in the context of official human gene nomenclature with additional molecular reference numbers and links. It is gene centered. That is, each record is a record of a gene or marker. If a study investigated 6 genes for a particular disorder, there will be 6 records. Anyone may view this database and anyone may submit records. You do not have to be an author on the original study to submit a record. All submitted records will be reviewed before inclusion in the archive. Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. From a comprehensive search of the MeSH annotation of MEDLINE articles, they identified 3,342 environmental etiological factors associated with 3,159 diseases. They also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the etiome, which they define as the comprehensive compendium of disease etiology.

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Cite this (HPO - Human Phenotype Ontology, RRID:SCR_006016)

URL: http://www.human-phenotype-ontology.org/

Resource Type: Resource, ontology, data or information resource, controlled vocabulary

A structured and controlled vocabulary for the phenotypic features encountered in human hereditary and other disease. The goal is to provide resource for the computational analysis of the human phenome, with a current focus on monogenic diseases listed in the Online Mendelian Inheritance in Man (OMIM) database, for which annotations are also provided. The HPO contains approximately 10,000 terms and over 50,000 annotations to hereditary diseases are available for download or can be browsed using the PhenExplorer. The HPO is being developed in collaboration with members of the OBO Foundry (Open Biological and Biomedical Ontologies), and logical definitions for HPO terms are being developed using PATO and a number of other ontologies including the FMA, GO, ChEBI, and MPATH. The HPO can be used for clinical diagnostics in human genetics (Phenomizer), bioinformatics research on the relationships between human phenotypic abnormalities and cellular and biochemical networks, for mapping between human and model organism phenotypes, and for providing a standardized vocabulary for clinical databases, among many other things. There exists a webpage for every HPO-term. The HPO project encourages input from the medical and genetics community with regards to the ontology itself and to clinical annotations.

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Cite this (Literature-derived human gene-disease network, RRID:SCR_005653)

URL: http://www.dbs.ifi.lmu.de/~bundschu/LHGDN.html

Resource Type: Resource, data or information resource, database

A text mining derived database with focus on extracting and classifying gene-disease associations with respect to several biomolecular conditions. It uses a machine learning based algorithm to extract semantic gene-disease relations from a textual source of interest. The semantic gene-disease relations were extracted with F-measures of 78. More specifically, the textual source utilized here originates from Entrez Gene''''s GeneRIF (Gene Reference Into Function) database (Mitchell, et al., 2003). LHGDN was created based on a GeneRIF version from March 31st, 2009, consisting of 414241 phrases. These phrases were further restricted to the organism Homo sapiens, which resulted in a total of 178004 phrases. We benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph. We extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining.

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    MGD

Cite this (MGD, RRID:SCR_012953)

URL: http://www.informatics.jax.org/

Resource Type: Resource, data or information resource, database

An integrated data resource for mouse genetic, genomic, and biological information. MGD includes a variety of data, ranging from gene characterization and genomic structures, to orthologous relationships between mouse genes and those of other mammalian species, to maps (genetic, cytogenetic, physical), to descriptions of mutant phenotypes, to characteristics of inbred strains, to information about biological reagents such as clones and primers. Data are accessed via search/retrieval Web forms and displayed as tables, text, and graphical maps, with supporting primary data. A rich set of hypertext links is provided, such as those from gene and clone information to DNA and protein sequence databases (GenBank, EMBL, DDBJ, SWISS-PROT), from bibliographic data to PubMed, from phenotypes to OMIM (Online Mendelian Inheritance in Man), and from gene homology records to the genomic databases of other species. MGD's data integration process places disparate data in contextual relationship, bringing together information from electronic downloads, the mouse Chromosome Committees, submissions from individual researchers, and actively curated data from the published literature. MGD encourages community participation via contributions of data and the development of consensus representations of the mouse genome. The resource's electronic bulletin boards are maintained to facilitate communication and collaboration among interested scientists. These Bulletin Boards provide a forum for community discussions and an excellent communication vehicle for MGD news. For more on the Mouse Genome Database follow the link, http://www.informatics.jax.org/mgihome/projects/overview.shtml.

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Cite this (National Cancer Institute Thesaurus, RRID:SCR_010370)

URL: http://purl.bioontology.org/ontology/NCIT

Resource Type: Resource, ontology, data or information resource, controlled vocabulary

A vocabulary for clinical care, translational and basic research, and public information and administrative activities.

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Cite this (National Center for Biomedical Ontology, RRID:SCR_003304)

URL: http://www.bioontology.org

Resource Type: Resource, database, organization portal, training resource, portal, data or information resource

Organization that provides biomedical researchers with online tools and a web portal enabling them to access, review, and integrate disparate ontological resources in all aspects of biomedical investigation and clinical practice. A major focus of the work involves the use of biomedical ontologies to aid in the management and analysis of data derived from complex experiments.

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Cite this (National Library of Medicine, RRID:SCR_011446)

URL: http://www.nlm.nih.gov/

Resource Type: Resource, government granting agency

NLM collects, organizes, and makes available biomedical science information to scientists, health professionals, and the public. The Library's Web-based databases, including PubMed/Medline and MedlinePlus, are used extensively around the world. NLM conducts and supports research in biomedical communications; creates information resources for molecular biology, biotechnology, toxicology, and environmental health; and provides grant and contract support for training, medical library resources, and biomedical informatics and communications research. Celebrating its 175th anniversary in 2011, the National Library of Medicine (NLM), in Bethesda, Maryland, is a part of the National Institutes of Health, U.S. Department of Health and Human Services (HHS). Since its founding in 1836 as the library of the U.S. Army Surgeon General, NLM has played a pivotal role in translating biomedical research into practice. It is the world's largest biomedical library and the developer of electronic information services that deliver trillions of bytes of data to millions of users every day. Scientists, health professionals, and the public in the United States and around the globe search the Library's online information resources more than 1 billion times each year. The Library is open to all and has many services and resources for scientists, health professionals, historians, and the general public. NLM has over 17 million books, journals, manuscripts, audiovisuals, and other forms of medical information on its shelves, making it the largest health-science library in the world. In today's increasingly digital world, NLM carries out its mission of enabling biomedical research, supporting health care and public health, and promoting healthy behavior by: * Acquiring, organizing, and preserving the world's scholarly biomedical literature; * Providing access to biomedical and health information across the country in partnership with the 5,800-member National Network of Libraries of Medicine (NN/LM); * Serving as a leading global resource for building, curating and providing sophisticated access to molecular biology and genomic information, including those from the Human Genome Project and NIH Common Fund; * Creating high-quality information services relevant to toxicology and environmental health, health services research, and public health; * Conducting research and development on biomedical communications systems, methods, technologies, and networks and information dissemination and utilization among health professionals, patients, and the general public; * Funding advanced biomedical informatics research and serving as the primary supporter of pre- and post-doctoral research training in biomedical informatics at 18 U.S. universities.

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    PsyGeNET

Cite this (PsyGeNET, RRID:SCR_014406)

URL: http://www.psygenet.org/web/PsyGeNET/menu;jsessionid=y6kqy9lqlxymr0nwwkkfo84

Resource Type: Resource, data analysis software, data processing software, database, software application, software resource, data or information resource

A resource for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a set of analysis tools and is the result of the integration of information from DisGeNET and data extracted from the literature by text mining, followed by curation by domain experts.

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Cite this (Semanticscience Integrated Ontology, RRID:SCR_010427)

URL: http://purl.bioontology.org/ontology/SIO

Resource Type: Resource, ontology, data or information resource, controlled vocabulary

Ontology that provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. It provides vocabulary for the Bio2RDF (http://bio2rdf.org) and SADI (http://sadiframework.org) projects.

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Cite this (Unified Medical Language System, RRID:SCR_006363)

URL: http://www.nlm.nih.gov/research/umls/

Resource Type: Resource, data access protocol, database, international standard specification, standard specification, narrative resource, web service, software resource, data or information resource

Database of key terminology, classification and coding standards, and associated resources to promote creation of more effective and interoperable biomedical information systems and services, including electronic health records. This set of files and software brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. Users can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators. The UMLS has three tools, which we call the Knowledge Sources: * Metathesaurus: Terms and codes from many vocabularies, including CPT, ICD-10-CM, LOINC, MeSH, RxNorm, and SNOMED CT * Semantic Network: Broad categories (semantic types) and their relationships (semantic relations) * SPECIALIST Lexicon and Lexical Tools: Natural language processing tools We use the Semantic Network and Lexical Tools to produce the Metathesaurus. Metathesaurus production involves: * Processing the terms and codes using the Lexical Tools * Grouping synonymous terms into concepts * Categorizing concepts by semantic types from the Semantic Network * Incorporating relationships and attributes provided by vocabularies * Releasing the data in a common format Although we integrate these tools for Metathesaurus production, you can access them separately or in any combination according to your needs. The UMLS Terminology Services (UTS) provides three ways to access the UMLS: Web Browsers, Local Installation, and Web Services APIs.

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