Name
Abstract | ||
|---|---|---|
Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://faculty.uaeu.ac.ae/nzaki/ProRank.htm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2330163.2330193 | GECCO |
Keywords | Field | DocType |
detecting protein complex,protein ranking algorithm,novel method,munich information center,protein-protein interaction,protein complex,protein sequence,benchmarked protein complex,recent method,protein protein interaction,computational biology | Data mining,Protein–protein interaction,Ranking,Protein sequencing,Computer science,Pagerank algorithm,Artificial intelligence,Information center,Machine learning | Conference |
Citations | PageRank | References |
14 | 0.60 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nazar Zaki | 1 | 139 | 14.31 |
| Jose Berengueres | 2 | 54 | 5.45 |
| Dmitry Efimov | 3 | 50 | 4.90 |