MedEvi: Retrieving textual evidence of relations between biomedical concepts from Medline.

@article{citeulike:2653988, abstract = {SUMMARY: Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/ CONTACT: kim@ebi.ac.uk, pezik@ebi.ac.uk.}, address = {EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.}, author = {Kim, Jung-Jae J. and Pezik, Piotr and Rebholz-Schuhmann, Dietrich }, citeulike-article-id = {2653988}, doi = {10.1093/bioinformatics/btn117}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {ebm, medline, pubmed}, month = {April}, posted-at = {2008-04-16 09:09:50}, priority = {2}, title = {MedEvi: Retrieving textual evidence of relations between biomedical concepts from Medline.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn117}, year = {2008} }

TY - JOUR ID - citeulike:2653988 TI - MedEvi: Retrieving textual evidence of relations between biomedical concepts from Medline. JF - Bioinformatics (Oxford, England) AD - EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK. SN - 1460-2059 N2 - SUMMARY: Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/ CONTACT: kim@ebi.ac.uk, pezik@ebi.ac.uk. KW - ebm KW - medline KW - pubmed AU - Kim, Jung-Jae AU - Pezik, Piotr AU - Rebholz-Schuhmann, Dietrich PY - 2008/04/09/ UR - http://dx.doi.org/10.1093/bioinformatics/btn117 ER -