Abstract
We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consisting of single residues as examples is affected by autocorrelation and we propose an ad-hoc remedy in which sequentially close pairs of candidate residues are classified as being jointly involved in the coordination of a zinc ion. We develop a kernel for this particular type of data that can handle variable length gaps between candidate coordinating residues. Our empirical evaluation on a data set of non redundant protein chains shows that explicit modeling the correlation between residues close in sequence allows us to gain a significant improvement in the prediction performance.
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Menchetti, S., Passerini, A., Frasconi, P., Andreini, C., Rosato, A. (2006). Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_26
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DOI: https://doi.org/10.1007/11732990_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33295-4
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