Tools   Select Another Resource Report Type

http://www.nactem.ac.uk/GREC/

A semantically annotated corpus of 240 MEDLINE abstracts (167 on the subject of E. coli species and 73 on the subject of the Human species) intended for training information extraction (IE) systems and/or resources which are used to extract events from biomedical literature. The corpus has been manually annotated with events relating to gene regulation by biologists. Each event is centered on either a verb (e.g. transcribe) or nominalized verb (e.g. transcription) and annotation consists of identifying, as exhaustively as possible, the structurally-related arguments of the verb or nominalized verb within the same sentence. Each event argument is then assigned the following information: * A semantic role from a fixed set of 13 roles which are tailored to the biomedical domain. * A biomedical concept type (where appropriate). The corpus in available for download in 2 formats: * A standoff format, based on the BioNLP'09 Shared Task format * An XML format, based on the GENIA event annotation format

Proper citation: GREC Corpus (RRID:SCR_006719) Copy   

•   Source: SciCrunch Registry

http://bioapps.sabanciuniv.edu/mugex/v02/

Service that automatically extracts mutation-gene pairs from MEDLINE abstracts for a given disease.

Proper citation: MuGeX (RRID:SCR_005306) Copy   

•   Source: SciCrunch Registry

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://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   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/webservices/whatizit/info.jsf

A text processing system that allows you to do textmining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search criteria are retrieved and processed by a pipeline of your choice. Whatizit is also available as 1) a webservice and as 2) a streamed servlet. The webservice allows you to enrich content within your website in a similar way as in the wikipedia. The streamed servlet allows you to process large amounts of text.

Proper citation: Whatizit (RRID:SCR_005824) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/facta/

Text mining tool to discover associations between biomedical concepts from MEDLINE articles. Use the service from your browser or via a Web Service. The whole MEDLINE corpus containing more than 20 million articles is indexed with an efficient text search engine, and it allows you to navigate such associations and their textual evidence in a highly interactive manner - the system accepts arbitrary query terms and displays relevant concepts immediately. A broad range of important biomedical concepts are covered by the combination of a machine learning-based term recognizer and large-scale dictionaries for genes, proteins, diseases, and chemical compounds. There is also a FACTA+ visualization service that can be found here: http://www.nactem.ac.uk/facta-visualizer/

Proper citation: FACTA+. (RRID:SCR_001767) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/medie/

An intelligent search engine to retrieve biomedical correlations from MEDLINE, based on indexing by Natural Language Processing and Text Mining techniques. You can find abstracts/sentences in MEDLINE by specifying semantics of correlations; for example, What activates p53 and What causes colon cancer. Semantic search uses a semantic query for finding biomedical correlations. Input a subject, a verb, and an object of a concept (or either of them) into a form. Results of the query will be shown in a second. (E.g., What does p53 activate? (subject=p53, verb=activate)) Reference: Miyao, Yusuke, Tomoko Ohta, Katsuya Masuda, Yoshimasa Tsuruoka, Kazuhiro Yoshida, Takashi Ninomiya and Jun''''ichi Tsujii (2006) Semantic Retrieval for the Accurate Identification of Relational Concepts in Massive Textbases. Proceedings COLING-ACL 2006. Sydney, Australia, pp. 1017--1024.

Proper citation: MEDIE (RRID:SCR_006254) Copy   

•   Source: SciCrunch Registry

http://www.nactem.ac.uk/Kleio/

An information retrieval system that provides knowledge enriched searching facilities across the ever growing MEDLINE collection, the world's most comprehensive source of life sciences and biomedical bibliographic information. The semantic faceted search, using named entity recognition, can be accessed from your browser. By combining a selection of software services they can provide enhanced results through a process that identifies key entities within the text, such as gene names or proteins, and improves the querying method with unique identifiers by automatically including synonyms, spelling variants and even disambiguating acronyms. This combines with the traditional features found in other interfaces to provide a much needed solution to the growing problem of finding valuable information within the ever increasing volume of modern publications. The current available categories: * PROTEIN, GENE, METABOLITE, DISEASE, SYMPTOM, ORGAN, * DIAG_PROC, THERAPEUTIC_PROC, (diagnostic/therapeutic procedure, e.g. MRI, cerebral blood flow) * GENERAL_PHENOM, HUMAN_PHENOM, NATURAL_PHENOM, (Medical phenomenon or process, e.g. UV radiation ) * INDICATOR (Reagent or diagnostic aid, e.g. hydrogen peroxide, sulfhydryl reagent) * ACRONYM, AUTHOR, PUBLICATIONTYPE (e.g. Journal Article, Technical Report) Reference: C. Nobata, P. Cotter, N. Okazaki, B. Rea, Y. Sasaki, Y. Tsuruoka, J. Tsujii and S. Ananiadou. Kleio: a knowledge-enriched information retrieval system for biology. In Proc. of the 31st Annual International ACM SIGIR Conference, pp. 787--788, 2008

Proper citation: KLEIO (RRID:SCR_000698) Copy   

•   Source: SciCrunch Registry

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   

•   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.ebi.ac.uk/Rebholz-srv/ebimed/

A web application that combines Information Retrieval and Extraction from Medline. EBIMed finds Medline abstracts in the same way PubMed does. Then it goes a step beyond and analyses them to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species. The results are shown in a table that displays all the associations and links to the sentences that support them and to the original abstracts. By selecting relevant sentences and highlighting the biomedical terminology EBIMed enhances your ability to acquire knowledge, relate facts, discover implications and, overall, have a good overview economizing the effort in reading.

Proper citation: EBIMed (RRID:SCR_005314) Copy   

•   Source: SciCrunch Registry

http://www.coremine.com/medical/#search

Service to access comprehensive information on diseases, drugs, treatments and medical biology. It is ideal for those seeking an overview of a complex subject while allowing the possibility to drill down to specific details. Search results are presented in a dashboard format comprized of panels containing various categories of information ranging from introductory sources to the latest scientific articles.

Proper citation: Coremine Medical (RRID:SCR_005323) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/CBBresearch/Wilbur/IRET/PIE/

A web service to extract Protein-protein interaction (PPI)-relevant articles from MEDLINE that provides protein interaction information (PPI) articles for biologists, baseline system performance for bio-text mining researchers and a compact PubMed-search environment for PubMed users. It accepts PubMed input formats including All Fields, Author, Journal, MeSH Terms, Publication Date, Title, and Title/Abstract with Boolean operations (AND, OR, and NOT). However, the output is the list of articles prioritized by PPI confidence rates. Some words (mostly gene/protein names) which contributed for PPI prediction are underlined and linked to Entrez or Entrez Gene. Even though our system focuses on a PubMed search environment, it also provides a CGI access for bio-text mining researchers. Using the CGI program, a list of PubMed IDs can be obtained as a query result, thus it can be utilized as a baseline system performance. PIE the search is based on a winning approach in the BioCreative III ACT competition (BC3)1. For input queries, MEDLINE articles are first retrieved through the PubMed service. PPI scores are calculated for the retrieved articles, and the articles are re-ranked based on scores. To effectively capture PPI patterns from biomedical literature, their approach utilizes both word and syntactic features for machine learning classifiers. Dependency parsing, gene mention tagging, and term-based features are utilized along with a Huber classifier.

Proper citation: PIE the search (RRID:SCR_005296) Copy   

•   Source: SciCrunch Registry

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

A controlled vocabulary thesaurus that consists of sets of terms naming descriptors in a hierarchical structure that permits searching at various levels of specificity. MeSH, in machine-readable form, is provided at no charge via electronic means. MeSH descriptors are arranged in both an alphabetic and a hierarchical structure. At the most general level of the hierarchical structure are very broad headings such as Anatomy or Mental Disorders. More specific headings are found at more narrow levels of the twelve-level hierarchy, such as Ankle and Conduct Disorder. There are 27,149 descriptors in 2014 MeSH. There are also over 218,000 entry terms that assist in finding the most appropriate MeSH Heading, for example, Vitamin C is an entry term to Ascorbic Acid. In addition to these headings, there are more than 219,000 headings called Supplementary Concept Records (formerly Supplementary Chemical Records) within a separate thesaurus. The MeSH thesaurus is used by NLM for indexing articles from 5,400 of the world''''s leading biomedical journals for the MEDLINE/PubMED database. It is also used for the NLM-produced database that includes cataloging of books, documents, and audiovisuals acquired by the Library. Each bibliographic reference is associated with a set of MeSH terms that describe the content of the item. Similarly, search queries use MeSH vocabulary to find items on a desired topic.

Proper citation: MeSH (RRID:SCR_004750) 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

http://www.cochrane.org/editorial-and-publishing-policy-resource/cochrane-central-register-controlled-trials-central

A bibliographic database that provides a highly concentrated source of reports of randomized controlled trials. Records contain the list of authors, the title of the article, the source, volume, issue, page numbers, and, in many cases, a summary of the article (abstract). They do not contain the full text of the article. Cochrane Groups maintain and update Specialized Registers, which are collections of controlled trials relevant to the groups. CENTRAL is comprised of these Specialized Registers, relevant records retrieved from MEDLINE and EMBASE, and records retrieved through handsearching (planned manual searching of a journal or conference proceedings to identify all reports of randomized controlled trials and controlled clinical trials). The Cochrane Collaboration contracts a technology company, Metaxis, to merge the records from the sources outlined above and provide a data feed to the publisher. New and changed data are delivered to the publisher on a monthly basis.

Proper citation: Cochrane Central Register of Controlled Trials (RRID:SCR_006576) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/books/NBK5330/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.Searchable book regarding molecular imaging and contrast agents (under development, in clinical trials or commercially available for medical applications) that have in vivo data (animal or human) published in peer-reviewed scientific journals prior to June 30 of 2013. 1444 agents are currently listed and there will be no more updates. Also available is a downloadable list of FDA approved contrast agents (Latest update: January 2013) and a Molecular Imaging Probes and Contrast Agents List (MIP & CA List) created by the MICAD staff by screening the PubMed / MedLine databases and other appropriate sources of such information. Only agents used in animal or human studies yielding in vivo data were selected for inclusion in the list. The list is by no means considered complete. No one imaging modality has been given preference over the others and the omission of any agent(s) or the introduction of any errors in the list is purely unintentional. The MIP & CA List is subject to the same copyright and disclaimers as the rest of the MICAD content. The database includes, but is not limited to, agents developed for positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), ultrasound (US), computed tomography (CT), optical imaging, planar radiography, and planar gamma imaging. The information on each agent is summarized in a book chapter format containing several sections such as Background, Synthesis, in vitro studies, Animal Studies (with sub-sections: rodents, other non-human primate animals, and human primates), Human Studies, and References. In addition, the references are linked to PubMed for retrieval of the publication abstract. Also, each chapter contains links to resources at the National Center for Biotechnology Information (NCBI) and other relevant databases regarding the target of the imaging probe or contrast agent.

Proper citation: Molecular Imaging and Contrast Agent Database (RRID:SCR_006712) Copy   

•   Source: SciCrunch Registry

http://xplormed.ogic.ca

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   

•   Source: SciCrunch Registry

http://www.chibi.ubc.ca/WhiteText/

Freely available corpus of manually annotated brain region mentions created to facilitate text mining of neuroscience literature. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. We encourage you to test new methods and applications of the dataset. Please contact us if you do, we would like to hear about and link to your work. The abstracts are from PubMed/Medline, specifically The Journal of Comparative Neurology.

Proper citation: Automated recognition of brain region mentions in neuroscience literature. (RRID:SCR_002731) Copy   

•   Source: SciCrunch Registry