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

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

This video was recorded at Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB), Tuusula 2006. 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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