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Therefore, the study of the current literature about a selected topic deposited in public databases, facilitates the generation of novel hypotheses associating a set of bioentities to a common context.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We created a text mining system (LAITOR:<jats:bold><jats:italic>L<\/jats:italic><\/jats:bold><jats:italic>iterature<\/jats:italic><jats:bold><jats:italic>A<\/jats:italic><\/jats:bold><jats:italic>ssistant for<\/jats:italic><jats:bold><jats:italic>I<\/jats:italic><\/jats:bold><jats:italic>dentification of<\/jats:italic><jats:bold><jats:italic>T<\/jats:italic><\/jats:bold><jats:italic>erms co-<\/jats:italic><jats:bold><jats:italic>O<\/jats:italic><\/jats:bold><jats:italic>ccurrences and<\/jats:italic><jats:bold><jats:italic>R<\/jats:italic><\/jats:bold><jats:italic>elationships<\/jats:italic>) that analyses co-occurrences of bioentities, biointeractions, and other biological terms in MEDLINE abstracts. The method accounts for the position of the co-occurring terms within sentences or abstracts. The system detected abstracts mentioning protein-protein interactions in a standard test (BioCreative II IAS test data) with a precision of 0.82-0.89 and a recall of 0.48-0.70. We illustrate the application of LAITOR to the detection of plant response genes in a dataset of 1000 abstracts relevant to the topic.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Text mining tools combining the extraction of interacting bioentities and biological concepts with network displays can be helpful in developing reasonable hypotheses in different scientific backgrounds.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-11-70","type":"journal-article","created":{"date-parts":[[2010,2,2]],"date-time":"2010-02-02T07:16:13Z","timestamp":1265094973000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["LAITOR - Literature Assistant for Identification of Terms co-Occurrences and Relationships"],"prefix":"10.1186","volume":"11","author":[{"given":"Adriano","family":"Barbosa-Silva","sequence":"first","affiliation":[]},{"given":"Theodoros G","family":"Soldatos","sequence":"additional","affiliation":[]},{"given":"Ivan LF","family":"Magalh\u00e3es","sequence":"additional","affiliation":[]},{"given":"Georgios A","family":"Pavlopoulos","sequence":"additional","affiliation":[]},{"given":"Jean-Fred","family":"Fontaine","sequence":"additional","affiliation":[]},{"given":"Miguel A","family":"Andrade-Navarro","sequence":"additional","affiliation":[]},{"given":"Reinhard","family":"Schneider","sequence":"additional","affiliation":[]},{"given":"J Miguel","family":"Ortega","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,2,1]]},"reference":[{"key":"3527_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/S0014-5793(00)01661-6","volume":"476","author":"MA Andrade","year":"2000","unstructured":"Andrade MA, Bork P: Automated extraction of information in molecular biology. 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