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https://neuinfo.org/mynif/search.php?q=nlx_149462&t=indexable&list=cover&nif=nlx_144509-1
A virtual database that indexes both BioNOT for negation data, and the Resource Discovery Pipeline: an automated resource discovery and semi-automated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. This virtual database currently indexes the following resources: * BioNOT, http://snake.ims.uwm.edu/bionot/index.php?searchterm=mecp2+autism&submit=Search * Resource Discovery Pipeline, http://lucene1.neuinfo.org/nif_resource/current/
Proper citation: Integrated Auto-Extracted Annotation (RRID:SCR_005892) Copy
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
http://www.oreganno.org/oregano/
Open source, open access database and literature curation system for community based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. Automatically cross referenced against PubMED, Entrez Gene, EnsEMBL, dbSNP, eVOC: Cell type ontology, and Taxonomy database. Community driven resource for curated regulatory annotation.
Proper citation: Open Regulatory Annotation Database (RRID:SCR_007835) Copy
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
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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