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http://www.ncbi.nlm.nih.gov/pubmed/
Public bibliographic database that provides access to citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. PubMed citations and abstracts include fields of biomedicine and health, covering portions of life sciences, behavioral sciences, chemical sciences, and bioengineering. Provides access to additional relevant web sites and links to other NCBI molecular biology resources. Publishers of journals can submit their citations to NCBI and then provide access to full-text of articles at journal web sites using LinkOut.
Proper citation: PubMed (RRID:SCR_004846) Copy
http://services.nbic.nl/copub/portal/
Text mining tool that detects co-occuring biomedical concepts in abstracts from the MedLine literature database. It allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs.
Proper citation: CoPub (RRID:SCR_005327) Copy
http://www.nactem.ac.uk/genia/
Resources and tools from a project to automatically extract useful information from texts written by scientists to help overcome the problems caused by information overload. The primary annotated resource created is the GENIA corpus, a collection of biomedical literature which consists of multiple layers of annotation, encompassing both syntactic and semantic annotation. The project also created or coordinated the annotation of multiple other corpus resources. Additionally, a rich set of automatic tools are available for various annotation tasks, most trained on various parts of the GENIA corpus annotations. The GENIA corpus was developed to provide a reference material for the development of bio-TM systems. The corpus currently contains 1,999 Medline abstracts which were collected using the three MeSH terms, human, blood cells, and transcription factors. The corpus has been annotated with various levels of linguistic and semantic information. The GENIA corpus includes the following: * POS annotation * Treebank * Coreference Annotation * Term annotation * Event annotation * Relation annotation * Cellular localization * Disease-Gene association * Pathway corpus The GENIA Project initiated the BioNLP Shared Task series and has organized a number of tasks in three different shared task events, many using resources based on GENIA Corpus annotations. Tools include: * XConc suite: a collection of XML-based tools which are integrated to support the corpus development and annotation.
Proper citation: GENIA Project: Mining literature for knowledge in molecular biology (RRID:SCR_007990) Copy
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13, 2026. Server that allows you to explore a set of abstracts derived from a MEDLINE search. The system gives you the main associations between the words in groups of abstracts. Then, you can select a subset of your abstracts based on selected groups of related words and iterate your analysis on them. The only input needed is a set of abstracts (in one of the currently accepted input formats) or a definition of how to obtain them. XplorMed is recommended for cases in which you do not know exactly what are you expecting to find. Your interests may be modified by the results obtained, or you may want to inquire new questions as the analysis develops. Also, the results may suggest you additional words that should be used to expand your query in MEDLINE (e.g., unexpected abbreviations of a protein name, or synonyms of a disease).
Proper citation: XplorMed (RRID:SCR_002549) Copy
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Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
