Name
Abstract | ||
|---|---|---|
In this paper, we present an association rule based protein interaction prediction method. We use neural network to cluster protein interaction data and feature selection method to reduce protein feature dimension. After this model training, association rules for protein interaction prediction are generated by decoding a set of learned weights of trained neural network and association rule mining. For model training, the initial network model was constructed with existing protein interaction data in terms of their functional categories and interactions. The protein interaction data of Yeast (S.cerevisiae) from MIPS and SGD are used. The prediction performance was compared with traditional simple association rule mining method. According to the experimental results, proposed method shows about 96.1% accuracy compared to simple association mining approach which achieved about 91.4%. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_77 | ICANN (2) |
Keywords | Field | DocType |
cluster protein interaction data,yeast protein-protein interaction,simple association mining approach,protein interaction data,traditional simple association rule,protein interaction prediction method,association rule mining,association rule,protein feature dimension,neural feature association rule,protein interaction prediction,model training,network model,protein protein interaction,feature selection,neural network | Data mining,Protein–protein interaction,Feature selection,Computer science,Artificial intelligence,Artificial neural network,Adaptive resonance theory,Pattern recognition,Expert system,Association rule learning,Decoding methods,Machine learning,Network model | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hong Eom | 1 | 86 | 8.91 |
| Byoung-Tak Zhang | 2 | 1571 | 158.56 |