|A major contributor to this article appears to have a close connection with its subject. (August 2011)|
eTBLAST is a free text similarity service search engine currently offering access to the MEDLINE database, the National Institutes of Health (NIH) CRISP database, the Institute of Physics (IOP) database, Wikipedia, arXiv, the NASA technical reports database, Virginia Tech class descriptions and a variety of databases of clinical interest. It is continuously expanding with additional text-based databases. eTBLAST searches citation databases and databases containing full text, such as PUBMED. The eTBLAST server compares a user's natural text query to target databases using a hybrid search algorithm consisting of a low-sensitivity weighted keyword-based first pass followed by a novel sentence-alignment based second pass. eTBLAST is a free web-based service of The Innovation Laboratory at the Virginia Bioinformatics Institute.
eTBLAST, as a text similarity engine, made possible a large study of duplicate publications and potential plagiarisms in the biomedical literature. Thousands of random samples of Medline abstracts were submitted to eTBLAST, and those with the highest similarity were studied and entered into an on-line database. This study is on-going, with the database maturing as the entries are manually inspected and classified. This work revealed several trends, including an increasing rate of duplication in the biomedical literature, as reported in the journals Bioinformatics, Anaesthesia and Intensive Care, Clinical Chemistry, Urologic Oncology, Nature, and Science.
Because eTBLAST is a text-similarity engine rather than a simple keyword-based search tool, it is claimed that the user need not identify and manipulate query keywords and Boolean operators, as must be done for other search engines.
eTBLAST aims to help the user rapidly to find references, evaluate novelty, find experts and journals in a given topical area and track the popularity of the topic as defined by the user’s query. There also is information found within the results as a set, in addition to those found within individual 'hits'. eTBLAST can also infer possible hypothese from inspection of implicit keywords found within the top most similar 'hits'. A matrix of similarity and a heat map are also displayed for the most similar 'hits'.
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