Abstract
Protein domains are the building blocks of proteins, and their interactions are crucial in forming stable protein-protein interactions (PPI) and take part in many cellular processes and biochemical events. Prediction of protein domain-domain interactions (DDI) is an emerging problem in computational biology. Different from early works on DDI prediction, which exploit only a single protein database, we introduce in this paper an integrative approach to DDI prediction that exploits multiple genome databases using inductive logic programming (ILP). The main contribution to biomedical knowledge discovery of this work are a newly generated database of more than 100,000 ground facts of the twenty predicates on protein domains, and various DDI findings that are evaluated to be significant. Experimental results show that ILP is more appropriate to this learning problem than several other methods. Also, many predictive rules associated with domain sites, conserved motifs, protein functions and biological pathways were found.
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Nguyen, T.P., Ho, T.B. (2006). Prediction of Domain-Domain Interactions Using Inductive Logic Programming from Multiple Genome Databases. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_20
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DOI: https://doi.org/10.1007/11893318_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
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