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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://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/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/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.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