IDPredictor : predict database links in biomedical database


Dateien zu dieser Ressource

Prüfsumme: MD5:2dd08b315603f65308925204471ec2c5

MEHLHORN, Hendrik, Matthias LANGE, Uwe SCHOLZ, Falk SCHREIBER, 2012. IDPredictor : predict database links in biomedical database. International Symposium on Integrative Bioinformatics. Hangzhou, China, 2. Apr 2012 - 4. Apr 2012. In: Journal of Integrative Bioinformatics - JIB ; 9,2. International Symposium on Integrative Bioinformatics. Hangzhou, China, 2. Apr 2012 - 4. Apr 2012, 190. eISSN 1613-4516

@inproceedings{Mehlhorn2012-06-26IDPre-38616, title={IDPredictor : predict database links in biomedical database}, year={2012}, doi={10.2390/biecoll-jib-2012-190}, booktitle={Journal of Integrative Bioinformatics - JIB ; 9,2}, author={Mehlhorn, Hendrik and Lange, Matthias and Scholz, Uwe and Schreiber, Falk}, note={Article Number: 190} }

<rdf:RDF xmlns:rdf="" xmlns:bibo="" xmlns:dc="" xmlns:dcterms="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:language>eng</dc:language> <dc:creator>Mehlhorn, Hendrik</dc:creator> <bibo:uri rdf:resource=""/> <dc:contributor>Lange, Matthias</dc:contributor> <dcterms:title>IDPredictor : predict database links in biomedical database</dcterms:title> <dc:contributor>Mehlhorn, Hendrik</dc:contributor> <dcterms:abstract xml:lang="eng">Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge except out of the interlinked databases. A prerequisite of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL:</dcterms:abstract> <dcterms:rights rdf:resource=""/> <dcterms:available rdf:datatype="">2017-04-26T09:13:55Z</dcterms:available> <dc:date rdf:datatype="">2017-04-26T09:13:55Z</dc:date> <dc:contributor>Schreiber, Falk</dc:contributor> <dcterms:issued>2012-06-26</dcterms:issued> <dc:creator>Schreiber, Falk</dc:creator> <dc:creator>Scholz, Uwe</dc:creator> <dc:contributor>Scholz, Uwe</dc:contributor> <dc:creator>Lange, Matthias</dc:creator> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 26.04.2017 (Informationen über die Zugriffsstatistik)

Mehlhorn_0-396389.pdf 21

Das Dokument erscheint in:

KOPS Suche


Mein Benutzerkonto