A discriminative approach to structured biological data

Simple item page
dc.contributor.authorMutter, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.coverage.spatialConference held at Hamilton, New Zealanden_NZ
dc.date.accessioned2014-03-14T03:45:33Z
dc.date.available2014-03-14T03:45:33Z
dc.date.issued2007
dc.description.abstractThis paper introduces the first author’s PhD project which has just got out of its initial stage. Biological sequence data is, on the one hand, highly structured. On the other hand there are large amounts of unlabelled data. Thus we combine probabilistic graphical models and semi-supervised learning. The former to handle structured data and latter to deal with unlabelled data. We apply our models to genotype-phenotype modelling problems. In particular we predict the set of Single Nucleotide Polymorphisms which underlie a specific phenotypical trait.en_NZ
dc.format.mimetypeapplication/pdf
dc.identifier.citationMutter, S. & Pfahringer, B. (2007). A discriminative approach to structured biological data. In Proceedings of NZCSRSC'07, the Fifth New Zealand Computer Science Research Student Conference. The University of Waikato, Hamilton, New Zealand, 10-13 April 2007, 2007(pp.1-4).en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/8564
dc.language.isoenen_NZ
dc.publisherThe University of Waikatoen_NZ
dc.relation.isPartOfProceedings of NZCSRSC'07, the Fifth New Zealand Computer Science Research Student Conference (online)en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectbioinformaticsen_NZ
dc.subjectprobabilistic graphical modelsen_NZ
dc.subjectsemi-supervised learningen_NZ
dc.subjectsingle nucleotide polymorphismen_NZ
dc.subjectMachine learning
dc.titleA discriminative approach to structured biological dataen_NZ
dc.typeConference Contributionen_NZ
pubs.begin-page1en_NZ
pubs.elements-id17306
pubs.end-page4en_NZ
pubs.finish-date2007-04-13en_NZ
pubs.start-date2007-04-10en_NZ
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
MutterNzcsrsc07_short.pdf
Size:
78.52 KB
Format:
Adobe Portable Document Format
Thumbnail Image
Name: MutterNzcsrsc07_short.pdf
Size: 78.52 KB
Kind: Adobe PDF
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Name: license.txt
Size: 1.71 KB
Kind: License
Collections
Computing and Mathematical Sciences Papers