Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition
Various emerging quantitative measurement technologies are producing genome, transcriptome and proteome-wide data collections which has motivated the de- velopment of data integration methods within an inferential framework. It has been demonstrated that for certain prediction tasks within computational biol- ogy synergistic improvements in performance can be obtained via integration of a number of (possibly heterogeneous) data sources. In  six different parameter representations of proteins were employed for fold recognition of proteins using Support Vector Machines (SVM).
Author: Mark Girolami, University Of Glasgow