LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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The prediction of particular types of protein-protein interactions (PPIs) based on knowledge of their interacting domains is a problem that has drawn the attention of researchers in the past few years. We focus on the prediction and analysis of obligate and nonobligate complexes by using structural domains from the CATH database. Our proposed prediction model is an extension of the one used in our previous work, which uses desolvation energies of domain-domain interactions (DDIs) present in the interfaces of such complexes. The prediction is performed via a support vector machine (SVM). Whereas previous efforts have considered structural domains taken from each level of the CATH hierarchy, in turn, we generalize this to allow some domains to be considered at one level of the hierarchy, while others may be considered at a different level. Our results show an improvement of 4.58% and 2.36% on two well-known datasets over our previous results.