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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
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
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
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