Meinicke, Peter

[["dc.bibliographiccitation.firstpage","369"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","International Journal of Neural Systems"],["dc.bibliographiccitation.lastpage","381"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Mersch, Britta"],["dc.contributor.author","Glasmachers, Tobias"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Igel, Christian"],["dc.date.accessioned","2018-11-07T10:58:07Z"],["dc.date.available","2018-11-07T10:58:07Z"],["dc.date.issued","2007"],["dc.description.abstract","Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernels for the detection of bacterial gene starts. The resulting kernels lead to higher classification rates, and the adapted parameters reveal the importance of particular triplets for classification, for example of those occurring in the Shine-Dalgarno Sequence."],["dc.identifier.doi","10.1142/S0129065707001214"],["dc.identifier.isi","000252398100003"],["dc.identifier.pmid","18098369"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/50408"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","World Scientific Publ Co Pte Ltd"],["dc.relation.issn","0129-0657"],["dc.title","Evolutionary optimization of sequence kernels for detection of bacterial gene starts"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","827"],["dc.bibliographiccitation.lastpage","836"],["dc.bibliographiccitation.seriesnr","4132"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Mersch, Britta"],["dc.contributor.author","Glasmachers, Tobias"],["dc.contributor.author","Meinicke, Peter"],["dc.contributor.author","Igel, Christian"],["dc.contributor.editor","Kollias, Stefanos"],["dc.date.accessioned","2018-11-07T10:29:55Z"],["dc.date.available","2018-11-07T10:29:55Z"],["dc.date.issued","2006"],["dc.description.abstract","Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernel for the detection of prokaryotic translation initiation sites. The resulting kernel leads to higher classification rates, and the adapted parameters reveal the importance for classification of particular triplets, for example of those occurring in the Shine-Dalgarno sequence."],["dc.identifier.doi","10.1007/11840930_86"],["dc.identifier.isi","000241475200086"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43746"],["dc.language.iso","en"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.publisher.place","Berlin, Heidelberg"],["dc.relation.conference","16th International Conference on Artificial Neural Networks (ICANN 2006)"],["dc.relation.crisseries","Lecture Notes in Computer Science"],["dc.relation.doi","10.1007/11840930"],["dc.relation.eventend","2006-09-14"],["dc.relation.eventlocation","Athens"],["dc.relation.eventstart","2006-09-10"],["dc.relation.isbn","3-540-38871-0"],["dc.relation.isbn","978-3-540-38873-9"],["dc.relation.ispartof","Artificial neural networks - ICANN 2006"],["dc.relation.ispartofseries","Lecture Notes in Computer Science; 4132"],["dc.relation.issn","0302-9743"],["dc.title","Evolutionary optimization of sequence kernels for detection of bacterial gene starts"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","216"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IEEE/ACM Transactions on Computational Biology and Bioinformatics"],["dc.bibliographiccitation.lastpage","226"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Igel, Christian"],["dc.contributor.author","Glasmachers, Tobias"],["dc.contributor.author","Mersch, Britta"],["dc.contributor.author","Pfeifer, Nico"],["dc.contributor.author","Meinicke, Peter"],["dc.date.accessioned","2018-11-07T11:03:31Z"],["dc.date.available","2018-11-07T11:03:31Z"],["dc.date.issued","2007"],["dc.description.abstract","Biological data mining using kernel methods can be improved by a task-specific choice of the kernel function. Oligo kernels for genomic sequence analysis have proven to have a high discriminative power and to provide interpretable results. Oligo kernels that consider subsequences of different lengths can be combined and parameterized to increase their flexibility. For adapting these parameters efficiently, gradient-based optimization of the kernel-target alignment is proposed. The power of this new, general model selection procedure and the benefits of fitting kernels to problem classes are demonstrated by adapting oligo kernels for bacterial gene start detection."],["dc.identifier.doi","10.1109/tcbb.2007.070208"],["dc.identifier.isi","000246071500006"],["dc.identifier.pmid","17473315"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51640"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee Computer Soc"],["dc.relation.issn","1545-5963"],["dc.title","Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]