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Disparate data fusion for protein phosphorylation prediction

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  • Genetha Gray
  • Pamela Williams
  • W. Brown
  • Jean-Loup Faulon
  • Kenneth Sale

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

Suggested Citation

  • Genetha Gray & Pamela Williams & W. Brown & Jean-Loup Faulon & Kenneth Sale, 2010. "Disparate data fusion for protein phosphorylation prediction," Annals of Operations Research, Springer, vol. 174(1), pages 219-235, February.
    • Handle: RePEc:spr:annopr:v:174:y:2010:i:1:p:219-235:10.1007/s10479-008-0347-9
    DOI: 10.1007/s10479-008-0347-9
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    1. Alan Aderem & Richard J. Ulevitch, 2000. "Toll-like receptors in the induction of the innate immune response," Nature, Nature, vol. 406(6797), pages 782-787, August.
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    Cited by:

    1. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    2. Şenay Yaşar Sağlam & W. Nick Street, 2018. "Distant diversity in dynamic class prediction," Annals of Operations Research, Springer, vol. 263(1), pages 5-19, April.

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