Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/24477
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dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.date.accessioned2022-11-21T08:17:58Z-
dc.date.available2022-11-21T08:17:58Z-
dc.date.issued2003-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/24477-
dc.description.abstractComputational analysis of biological sequences – linear descriptions of protein, DNA and RNA molecules has completely changed its character since the late 1980s. The main driving force behind the changes has been the introduction of new, efficient experimental techniques, primarily DNA sequencing that has led to an exponential growth of data. As genome and other sequencing projects continue to advance, the interest progressively switches from the accumulation of data to its interpretation. There are some problems concerning the vast amount of data in the biological databases that has to be taken into account.en_US
dc.publisherInstitute of Informatics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University in Skopje, Macedoniaen_US
dc.subjectbioinformatics, machine learning, SVM, neural networksen_US
dc.titleBioinformatics–the Machine Learning Approachen_US
dc.typeProceedingsen_US
dc.relation.conferenceCIIT 2003en_US
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item.grantfulltextopen-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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