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http://www.disgenet.org

Database and discovery platform containing publicly available collections of genes and variants associated to human diseases. Integrates data from curated repositories, GWAS catalogues, animal models and scientific literature.

Proper citation: DisGeNET (RRID:SCR_006178) Copy   

•   Source: SciCrunch Registry

http://www.upf.edu/en/

Public university in Spain that offers programs in social sciences and humanities, health and life sciences, international training in communication and communication sciences. Has colleges based on topics such as law, translation and interpretation, and engineering. Research is organized into economics and business, experimental sciences and health, law.

Proper citation: Pompeu Fabra University; Barcelona; Spain (RRID:SCR_000256) Copy   

•   Source: SciCrunch Registry

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

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.

Proper citation: Unified Medical Language System (RRID:SCR_006363) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

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

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.

Proper citation: Literature-derived human gene-disease network (RRID:SCR_005653) Copy   

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

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.

Proper citation: Semanticscience Integrated Ontology (RRID:SCR_010427) Copy   

•   Source: SciCrunch Registry

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

Knowledge platform on psychiatric disorders and their genes. Resource for exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of database and 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.

Proper citation: PsyGeNET (RRID:SCR_014406) Copy   

•   Source: SciCrunch Registry

http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry

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

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.

Proper citation: National Library of Medicine (RRID:SCR_011446) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/gene

Database for genomes that have been completely sequenced, have active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. Includes nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases. All entries follow NCBI's format for data collections. Content of Entrez Gene represents result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. Content is updated as new information becomes available.

Proper citation: Entrez Gene (RRID:SCR_002473) Copy   

•   Source: SciCrunch Registry

https://www.hmtphenome.uniba.it

Collection of data about variants, genes, phenotypes and diseases involved in mitochondrial functionality. Users can search for variant position, gene, phenotype or disease and retrieve all related information through integrated network of biological entities.

Proper citation: HmtPhenome (RRID:SCR_017289) Copy   

•   Source: SciCrunch Registry

http://cytoscape.org

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

Proper citation: Cytoscape (RRID:SCR_003032) Copy   

•   Source: SciCrunch Registry

http://www.bioontology.org

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.

Proper citation: National Center for Biomedical Ontology (RRID:SCR_003304) Copy   

•   Source: SciCrunch Registry

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

Provides standardized vocabulary of phenotypic abnormalities encountered in human disease. Structured and controlled vocabulary for phenotypic features encountered in human hereditary and other disease. HPO is being developed in collaboration with members of 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 FMA, GO, ChEBI, and MPATH.

Proper citation: Human Phenotype Ontology (RRID:SCR_006016) Copy   

•   Source: SciCrunch Registry

http://www.disgenet.org/ds/DisGeNET/files/GeneDiseaseAssociation.owl

Ontology that describes the different types of associations between a gene and a disease. It was developed to integrate information from different databases that contain gene-disease associations such as UniProt, CTD, Orphanet, the GWAS Catalog, GAD, MGD, RGD, and LHGDN.

Proper citation: Gene-Disease Association Type Ontology (RRID:SCR_006159) Copy   

•   Source: SciCrunch Registry

http://ctdbase.org/

A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu

Database for genetic, genomic, phenotype, and disease data generated from rat research. Centralized database that collects, manages, and distributes data generated from rat genetic and genomic research and makes these data available to scientific community. Curation of mapped positions for quantitative trait loci, known mutations and other phenotypic data is provided. Facilitates investigators research efforts by providing tools to search, mine, and analyze this data. Strain reports include description of strain origin, disease, phenotype, genetics, immunology, behavior with links to related genes, QTLs, sub-strains, and strain sources.

Proper citation: Rat Genome Database (RGD) (RRID:SCR_006444) Copy   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/bsd/pmresources.html

A premier bibliographic database that contains over 18 million references to journal articles in life sciences with a concentration on biomedicine. A distinctive feature is that the records are indexed with NLM Medical Subject Headings (MeSH). PubMed provides free access to MEDLINE and links to full text articles when possible. The great majority of journals are selected for MEDLINE based on the recommendation of the Literature Selection Technical Review Committee (LSTRC), an NIH-chartered advisory committee of external experts analogous to the committees that review NIH grant applications. Some additional journals and newsletters are selected based on NLM-initiated reviews, e.g., history of medicine, health services research, AIDS, toxicology and environmental health, molecular biology, and complementary medicine, that are special priorities for NLM or other NIH components. These reviews generally also involve consultation with an array of NIH and outside experts or, in some cases, external organizations with which NLM has special collaborative arrangements. MEDLINE is the primary component of PubMed, part of the Entrez series of databases provided by the NLM National Center for Biotechnology Information (NCBI). MEDLINE may also be searched via the NLM Gateway. Time coverage: generally 1946 to the present, with some older material. Source: Currently, citations from approximately 5,516 worldwide journals in 39 languages; 60 languages for older journals. Citations for MEDLINE are created by the NLM, international partners, and collaborating organizations.

Proper citation: MEDLINE (RRID:SCR_002185) Copy   

•   Source: SciCrunch Registry

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

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

Proper citation: National Cancer Institute Thesaurus (RRID:SCR_010370) Copy   

•   Source: SciCrunch Registry

https://3dvcell.ncbi.nlm.nih.gov/

THIS RESOURCE IS NO LONGER IN SERVICE, confirmed by curator 11/21/2018; Community of researchers attempting to build a comprehensive virtual cell model. The 3DVC will do for cell biology what the Large Hadron Collider (LHC) does for particle physics, but through a virtual rather than physical resource. It will bring together collaborators around a shared infrastructure to advance the field through efficient groundbreaking science and technology, the results of which will be broadly disseminated to an audience ranging from K12 to professionals. The 3DVC is committed to open science, yet strives for sustainability through new business models that leverages that open content.

Proper citation: 3DVC (RRID:SCR_001377) Copy   

•   Source: SciCrunch Registry