Name
Title | ||
|---|---|---|
Identifying protein-protein interaction sites using granularity computing of quotient space theory |
Abstract | ||
|---|---|---|
The function of protein-protein interaction is very important to cell activity. Studying protein-protein interaction can help us understand life activities and pharmaceutical design. In this study, a kernel covering algorithm combined with the theory of granular computing of quotient space for predicting protein-protein interaction sites is proposed, (i.e. KCA-GS Model). This method achieves good performances, and the Sensitivity, Specificity, Accuracy and Correlation coefficient are 52.97%, 53.92%, 70.27%, 24.61%, respectively. It is indicated that our method is effective, potential and promising to identify protein-protein interaction sites. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16248-0_103 | RSKT |
Keywords | Field | DocType |
protein-protein interaction site,life activity,protein-protein interaction,cell activity,good performance,correlation coefficient,granular computing,granularity computing,quotient space theory,pharmaceutical design,quotient space,entropy,protein protein interaction,effective potential | Kernel (linear algebra),Correlation coefficient,Protein–protein interaction,Pattern recognition,Quotient space (topology),Quotient space theory,Granular computing,Artificial intelligence,Granularity,Mathematics | Conference |
Volume | ISSN | ISBN |
6401 | 0302-9743 | 3-642-16247-9 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanping Zhang | 1 | 0 | 0.34 |
| Yongcheng Wang | 2 | 31 | 7.30 |
| Jun Ma | 3 | 0 | 0.34 |
| Xiaoyan Chen | 4 | 0 | 0.34 |