Name
Abstract | ||
|---|---|---|
The importance of detecting protein complexes in protein interaction networks originates from the fact that they are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Despite the notable performance of the current computational methods for detecting protein complexes, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of complexes. In this paper, we present “DyCluster”, a framework to model dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/CIBCB.2015.7300271 | 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Keywords | Field | DocType |
protein complex detection,gene expression bicluster,protein interaction network,cellular process,abnormal molecular event,DyCluster,gene expression data,biclustering technique,complex-detection algorithm | Protein Interaction Networks,Computer science,Gene expression,Artificial intelligence,Biclustering,Bioinformatics,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eileen Marie Hanna | 1 | 10 | 1.11 |
| Nazar Zaki | 2 | 139 | 14.31 |