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

@article{citeulike:2477596, 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 hasn't 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 paper 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/ Supplementary materials are available at http://miron.ibb.waw.pl/elise.supplementary/ Contact: piotr@ibb.waw.pl 10.1093/bioinformatics/btn086}, author = {Gladki, Arek and Siedlecki, Pawel and Kaczanowski, Szymon and Zielenkiewicz, Piotr }, citeulike-article-id = {2477596}, doi = {10.1093/bioinformatics/btn086}, journal = {Bioinformatics}, keywords = {pubmed}, month = {March}, pages = {btn086+}, posted-at = {2008-03-06 07:48:23}, priority = {2}, title = {e-LiSe - an online tool for finding needles in the "(Medline) haystack"}, url = {http://dx.doi.org/10.1093/bioinformatics/btn086}, year = {2008} }

TY - JOUR ID - citeulike:2477596 L3 - citeulike-article-id:2477596 TI - e-LiSe - an online tool for finding needles in the "(Medline) haystack" JF - Bioinformatics SP - btn086 N2 - 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 hasn't 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 paper 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/ Supplementary materials are available at http://miron.ibb.waw.pl/elise.supplementary/ Contact: piotr@ibb.waw.pl 10.1093/bioinformatics/btn086 KW - pubmed AU - Gladki, Arek AU - Siedlecki, Pawel AU - Kaczanowski, Szymon AU - Zielenkiewicz, Piotr PY - 2008/03/05/ UR - http://dx.doi.org/10.1093/bioinformatics/btn086 ER -