Name
Abstract | ||
|---|---|---|
Study on protein-protein interaction is important for understanding the protein function in cell activity. Protein-protein recognition plays a crucial role of biology. Therefore, we use the properties of protein interface for protein recognition prediction because the interface offers important clues in biological functions. Genetic Programming (GP), one of artificial intelligence technologies, has been proposed in data classification research in biology. In this paper, we present a prediction method with GP for protein-protein recognition based on protein binding site features. We successfully predict recognition proteins with an average accuracy rate of 78% with ten-fold cross validation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1655925.1656059 | Int. Conf. Interaction Sciences |
Keywords | Field | DocType |
genetic programming,protein function,protein-protein interaction,protein recognition prediction,protein binding site feature,protein-protein recognition,important clue,protein interface,prediction method,recognition protein,prediction model,protein protein interaction,protein complex,artificial intelligent,feature vector,binding site,protein binding,cross validation,activator protein | Plasma protein binding,Feature vector,Binding site,Computer science,Pattern recognition receptor,Protein protein,Genetic programming,Artificial intelligence,Data classification,Cross-validation,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.38 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huang-Cheng Kuo | 1 | 42 | 23.87 |
| Kuan-Yu Su | 2 | 5 | 0.78 |
| Ping-Lin Ong | 3 | 2 | 0.74 |
| Jen-Peng Huang | 4 | 57 | 6.45 |