e-LiSe--an online tool for finding needles in the '(Medline) haystack'.
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'.


BACKGROUND
The software is available at http://miron.ibb.waw.pl/elise/
Gladki
Arek
A
Bioinformatics Department, Institute of Biochemistry and Biophysics, Polish Academy of Sciences, ul. Pawinskiego 5a, 02-106, Warszawa, Poland.
Siedlecki
Pawel
P
Kaczanowski
Szymon
S
Zielenkiewicz
Piotr
P
D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D009862:Online Systems; D010506:Periodicals as Topic; D012984:Software; D014584:User-Computer Interface; D018875:Vocabulary, Controlled