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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maleki, M. Hall, M. Rueda, L. |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Computer Science, University of Windsor, 401 Sunset Ave., ON, N9B3P4, Canada (Maleki, M.; Hall, M.; Rueda, L.) |
| Abstract | The identification and 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 non-obligate complexes by using structural domains from the CATH database. Our proposed prediction model uses desolvation energies of domain-domain interactions (DDIs) present in the interfaces of such complexes. The prediction is performed via linear dimensionality reduction (LDR) and support vector machines (SVMs). Our results on two well-known datasets show that DDI features of the first three levels of CATH, especially level 2, are more powerful and discriminative than features of other levels in predicting these types of complexes. Furthermore, a detailed analysis shows that different DDIs are present in obligate and non-obligate complexes, and that homo-DDIs are more likely to be present in obligate interactions. |
| Starting Page | 9 |
| Ending Page | 15 |
| File Size | 1230582 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467311908 |
| e-ISBN | 9781467311915 |
| DOI | 10.1109/CIBCB.2012.6217204 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Proteins Amino acids Accuracy Vectors Kernel Transient analysis complex type prediction protein-protein interaction domain-domain interaction CATH |
| Content Type | Text |
| Resource Type | Article |
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