K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space |
| |
Authors: | Max Bylesjö Mattias Rantalainen Jeremy K Nicholson Elaine Holmes Johan Trygg |
| |
Affiliation: | 1.Research Group for Chemometrics, Department of Chemistry,Ume? University,Ume?,Sweden;2.Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Faculty of Medicine,Imperial College,London,UK |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|