Data mining meets bioinformatics : automated interpretation of complex functional relationships in combination with DNA chip technology

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dc.contributor Division de_CH
dc.contributor Institut für Physikalische und Theoretische Chemie de_DE
dc.contributor.author Dubitzky, W. de_DE
dc.contributor.author Bulashevska, S. de_DE
dc.contributor.author Berrar, D. de_DE
dc.contributor.author Conrad, C. de_DE
dc.contributor.author Granzow, M. de_DE
dc.contributor.author Eils, R. de_DE
dc.contributor.other Gauglitz, Günter de_DE
dc.date.accessioned 2001-11-08 de_DE
dc.date.accessioned 2014-03-18T10:09:19Z
dc.date.available 2001-11-08 de_DE
dc.date.available 2014-03-18T10:09:19Z
dc.date.issued 2001 de_DE
dc.identifier.other 099400707 de_DE
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-3292 de_DE
dc.identifier.uri http://hdl.handle.net/10900/48220
dc.description.abstract Recent years have seen a dramatic increase in the amount of genetic information stored in electronic format. It has been estimated that the amount of information in genomics and proteomics doubles every 20 months and the size and number of databases are increasing even faster. It is widely accepted that a sophisticated exploration of such data is crucial in a variety of fields such as disease genetics and pharmacogenomics. While both corporate and institutional efforts have concentrated on the integration of heterogeneous data in genomics and proteomics, a systematic data exploration is still at its beginning. Although data mining has celebrated many successes in business operations applications as retail and marketing, its application to scientific and engineering data is not straightforward. Data sets in life sciences are often significantly larger in volume, structurally more complex then traditional business data, and often rapidly changing in time. In contrast to business environments, the body of existing background knowledge in life sciences is extensive. We will report on recent efforts to adapt data mining technology for effective knowledge discovery in life sciences. As one example, we will describe how DNA chip technology, data mining and molecular genetic expert knowledge can be combined for effective discovery of complex functional relationships between genotypic information on one hand and clinical parameters on the other hand. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-nopod de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_ubt-nopod.php?la=en en
dc.subject.classification Biosensor , Array-Technologie , Bioinformatik de_DE
dc.subject.ddc 540 de_DE
dc.subject.other Data-mining en
dc.title Data mining meets bioinformatics : automated interpretation of complex functional relationships in combination with DNA chip technology en
dc.type Sonstiges de_DE
dc.date.updated 2010-02-10 de_DE
utue.publikation.fachbereich Sonstige - Chemie und Pharmazie de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
dcterms.DCMIType Text de_DE
utue.publikation.typ report de_DE
utue.opus.id 329 de_DE
utue.publikation.source http://barolo.ipc.uni-tuebingen.de/biosensor2001/ de_DE

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