uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Proteochemometrics modelling of receptor ligand interactions using rough sets
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology.
Show others and affiliations
2004 (English)In: Proceedings of the German conference on Bioinformatics / [ed] Robert Giegerich, Jens Stoye, 2004, 85-94 p.Conference paper, Published paper (Refereed)
Abstract [en]

We report on a model for the interaction of chimeric melanocortin G-protein coupled receptors with peptide ligands using the rough set approach. Rough sets generate If-Then rule models using Boolean reasoning. Two separate datasets have been analyzed, for which the binding affinities have previously been measured experimentally. The receptors and ligands are described by vectors of strings. Different partitions of each dataset were evaluated in order to find an optimal partition into rough set decision classes. To obtain a measurement of the accuracy of the rough set classifier generated from each dataset, a 10-fold cross validation (CV) was performed. The Area Under Curve (AUC) was calculated for each iteration during CV. This resulted in an AUC mean of 0.94 (SD 0.12) and 0.93 (SD 0.16) for the first and second dataset respectively. The CV results show that the rough set models exhibit a high classification quality. The decision rules generated from the rough set model inductions are easy to interpret. We apply this information to develop models of the interaction between ligands and receptors.

Place, publisher, year, edition, pages
2004. 85-94 p.
National Category
Biological Sciences Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-79855ISBN: 3-88579-382-2 (print)OAI: oai:DiVA.org:uu-79855DiVA: diva2:107769
Available from: 2006-06-29 Created: 2006-06-29 Last updated: 2011-01-03Bibliographically approved

Open Access in DiVA

No full text

Authority records BETA

Strömbergsson, Helena

Search in DiVA

By author/editor
Strömbergsson, Helena
By organisation
The Linnaeus Centre for BioinformaticsPharmaceutical Pharmacology
Biological SciencesPharmaceutical Sciences

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 513 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf