Abstract
We presented a novel algorithm called DPCWN (Detecting Protein Complexes based on Sequence Information in Weighted PPI Network) to discover protein complexes from the weighted PPI network. In the algorithm, biological information (protein amino acid background frequency) is introduced to rank cluster and the combination of density, network diameter and included angle cosine is used to locate the complexes. First, compared with other three competing methods, the experimental results demonstrated that DPCWN can achieve better performance. Second, the feasibility and the improved performance by adding the biological properties are proved. Third, the detected clusters are validated by biological function annotation. Finally, the robustness of our algorithm is demonstrated by the experimental reports. Therefore, DPCWN can provide a useful framework for the study of more comprehensive properties in the biological network.
| Original language | English |
|---|---|
| Pages (from-to) | 1565-1570 |
| Number of pages | 6 |
| Journal | Journal of Computational and Theoretical Nanoscience |
| Volume | 9 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2012 |
| Externally published | Yes |
Keywords
- Background Frequency
- Protein Complex
- Weighted PPI Network
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