Disparate data fusion for protein phosphorylation prediction

Genetha Gray (), Pamela Williams (), W. Brown (), Jean-Loup Faulon () and Kenneth Sale ()

Annals of Operations Research, 2010, vol. 174, issue 1, 219-235

Abstract: New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method is an extension of current ensemble classification techniques and has the advantage of allowing data to remain in heterogeneous databases. In this paper, we focus on the applicability of such a framework to the protein phosphorylation prediction problem. Copyright Springer Science+Business Media, LLC 2010

Keywords: Ensemble classification; Phosphorylation; Base classifier; Fusion (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/s10479-008-0347-9

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