The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

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doi doi:10.2390/biecoll-jib-2010-110
submission October 26, 2009
last revision December 09, 2009
published January 15, 2010
NCBI PubMed PubMed ID 20134080

Matthias Lange, Karl Spies, Joachim Bargsten, Gregor Haberhauer, Matthias Klapperstück, Michael Leps, Christian Weinel, Röbbe Wünschiers, Mandy Weißbach, Jens Stein and Uwe Scholz

Correspondence should be addressed to:
Matthias Lange
Bioinformatics and Information Technology, IPK Gatersleben, Corrensstraße 3, D-06466 Gatersleben, Germany
ed.nebelsretag-kpi@nullegnal


Abstract

Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented. In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

Reference

Matthias Lange, Karl Spies, Joachim Bargsten, Gregor Haberhauer, Matthias Klapperstück, Michael Leps, Christian Weinel, Röbbe Wünschiers, Mandy Weißbach, Jens Stein and Uwe Scholz. The LAILAPS Search Engine: Relevance Ranking in Life Science Databases. Journal of Integrative Bioinformatics, 7(2):110, 2010. Online Journal: http://journal.imbio.de/index.php?paper_id=110
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