Thumbnail
Access Restriction
Open

Author Sing, Tobias ♦ Beerenwinkel, Niko ♦ Lengauer, Thomas
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Different Effect ♦ Localized Rule ♦ Statistical Learning ♦ Molecular Biology ♦ Entire Instance Space ♦ Propositional Rule ♦ Different Genetic Background ♦ Roc Curve ♦ Particular Mutational Pattern ♦ Model Class ♦ Mixture Model ♦ Genetic Background
Description We introduce a model class for statistical learning which is based on mixtures of propositional rules. In our mixture model, the weight of a rule is not uniform over the entire instance space. Rather, it depends on the instance at hand. This is motivated by applications in molecular biology, where it is frequently observed that the effect of a particular mutational pattern depends on the genetic background in which it occurs. We assume in our model that the effect of a given pattern of mutations will be very similar only among sequences that are also highly similar to each other. On the other hand, a pattern might have very different effects in different genetic backgrounds.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2004-01-01
Publisher Institution In et al José Hernández-Orallo, editor, 1st International Workshop on ROC Analysis in Artificial Intelligence