Name
Abstract | ||
|---|---|---|
Protein–protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccharomyces cerevisiae show that the clusters obtained by the genetic approach are a larger number of those found by RNSC, though this method predicts more true complexes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.06.061 | Neurocomputing |
Keywords | DocType | Volume |
Protein–protein interaction networks clustering,Evolutionary approaches,Genetic algorithms | Journal | 145 |
ISSN | Citations | PageRank |
0925-2312 | 1 | 0.35 |
References | Authors | |
30 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clara Pizzuti | 1 | 1250 | 122.13 |
| Simona E. Rombo | 2 | 192 | 22.21 |