Detecting Protein Complexes Based on Sequence Information in the Weighted Protein–Protein Interaction Network

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.

Protein complex identification based on weighted PPI network with multi-source information

“…Then weighted graphs are constructed by Gene Ontology (GO) and von Mering data consisting of protein interactions critically evaluated ( von Mering et al, 2002 ). Lastly, we apply protein identification method on these new weighted graphs based on our continuing work ( Yu et al, 2017 ;Yu et al, 2012 ) and the results indicate that the proposed framework is effective. Section 2 introduces the https://doi.org/10.1016/j.jtbi.2019.06.005 0022-5193/© 2019 Elsevier Ltd. All rights reserved.…”