Name
Abstract | ||
|---|---|---|
The course of developing effective medical treatments is typically based on the identification of disease-triggering protein complexes. In this paper, we present ProRank+, an effective method for detecting protein complexes in protein interaction networks. By assuming that complexes may overlap, the method uses a ranking algorithm to order proteins based on their importance in the network. In addition, a novel merging procedure is introduced to refine the predicted complexes in terms of their members. The experimental studies and results showed that ProRank+ outperforms several state-of-the-art methods in terms of the number of correctly-detected protein complexes using numerous quality measures. |
Year | Venue | Keywords |
|---|---|---|
2014 | BIOINFORMATICS 2014: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS | Google PageRank Algorithm,PPI,Protein Complex,Essential Protein,ProRank Algorithm |
Field | DocType | Citations |
Protein Interaction Networks,Computer science,Artificial intelligence,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.35 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eileen Marie Hanna | 1 | 10 | 1.11 |
| Nazar Zaki | 2 | 139 | 14.31 |