K-OPLS package: kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space.

ABSTRACT:

Background

Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.

Results

We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects

SUBMITTER: Bylesjo M 

PROVIDER: S-EPMC2323673 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature