Name
Abstract | ||
|---|---|---|
In this paper, we presents an adaptive neural network based clustering method to group protein–protein interaction data according to their functional categories for new protein interaction prediction in conjunction with information theory based feature selection. Our technique for grouping protein interaction is based on ART-1 neural network. The cluster prototype constructed with existing protein interaction data is used to predict the class of new protein interactions. The protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD are used. The clustering performance was compared with traditional k-means clustering method in terms of cluster distance. According to the experimental results, the proposed method shows about 89.7% clustering accuracy and the feature selection filter boosted overall performances about 14.8%. Also, inter-cluster distances of cluster constructed with ART-1 based clustering method have shown high cluster quality. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30561-3_6 | CIT |
Keywords | Field | DocType |
grouping protein interaction,cluster distance,yeast protein,protein interaction data,clustering performance,new protein interaction prediction,clustering method,new protein interaction,clustering accuracy,group protein,adaptive neural,cluster prototype,neural network,protein protein interaction,feature selection,k means clustering | Protein–protein interaction,Feature selection,Computer science,Consensus clustering,Artificial intelligence,Artificial neural network,Cluster analysis,Distributed computing,Information theory,k-means clustering,Pattern recognition,Correlation clustering,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-24126-4 | 3 | 0.45 |
References | Authors | |
10 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hong Eom | 1 | 86 | 8.91 |
| Byoung-Tak Zhang | 2 | 1571 | 158.56 |