Abstract
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.
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Nassif, H., Al-Ali, H., Khuri, S., Keirouz, W., Page, D. (2010). An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_14
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DOI: https://doi.org/10.1007/978-3-642-13840-9_14
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