Disparate data fusion for protein phosphorylation prediction
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 fo...
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| Veröffentlicht in: | Annals of operations research 2010-02, Vol.174 (1), p.219-235 |
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| container_title | Annals of operations research |
| container_volume | 174 |
| creator | Gray, Genetha A. Williams, Pamela J. Brown, W. Michael Faulon, Jean-Loup Sale, Kenneth L. |
| description | 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. |
| doi_str_mv | 10.1007/s10479-008-0347-9 |
| format | Article |
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| ispartof | Annals of operations research, 2010-02, Vol.174 (1), p.219-235 |
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| source | EBSCOhost Business Source Complete; SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Amino acids Business and Management Cell division Classification Combinatorics Data collection Data mining Datasets Enzymes Gene expression Hypotheses Investigations Kinases Laboratories Methods Operations research Operations Research/Decision Theory Phosphorylation Proteins Proteomics Signal transduction Studies Theory of Computation |
| title | Disparate data fusion for protein phosphorylation prediction |
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