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Bioinformatics Advance Access originally published online on March 5, 2008
Bioinformatics 2008 24(8):1115-1117; doi:10.1093/bioinformatics/btn086
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

e-LiSe—an online tool for finding needles in the ‘(Medline) haystack’

Arek Gladki 1, Pawel Siedlecki 1,2, Szymon Kaczanowski 1 and Piotr Zielenkiewicz 1,2,*

1Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106 and 2Plant Molecular Biology Department, Warsaw University, Warszawa, Poland

*To whom correspondence should be addressed.


  Abstract

Summary: Using literature databases one can find not only known and true relations between processes but also less studied, non-obvious associations. The main problem with discovering such type of relevant biological information is ‘selection’. The ability to distinguish between a true correlation (e.g. between different types of biological processes) and random chance that this correlation is statistically significant is crucial for any bio-medical research, literature mining being no exception. This problem is especially visible when searching for information which has not been studied and described in many publications. Therefore, a novel bio-linguistic statistical method is required, capable of ‘selecting’ true correlations, even when they are low-frequency associations. In this article, we present such statistical approach based on Z-score and implemented in a web-based application ‘e-LiSe’.

Availability: The software is available at http://miron.ibb.waw.pl/elise/

Contact: piotr{at}ibb.waw.pl

Supplementary information: Supplementary materials are available at http://miron.ibb.waw.pl/elise/supplementary/

Associate Editor: Alfonso Valencia

Received on August 2, 2007; revised on February 25, 2008; accepted on March 3, 2008

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