Bayesian Data Fusion with Gaussian Process Priors : An Application to Protein Fold Recognition

author: Mark Girolami, School of Computing Science, University of Glasgow
published: Feb. 25, 2007,   recorded: June 2006,   views: 7536
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Description

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 [1] six different parameter representations of proteins were employed for fold recognition of proteins using Support Vector Machines (SVM).

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