Name
Abstract | ||
|---|---|---|
Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s10916-006-7402-3 | J. Medical Systems |
Keywords | Field | DocType |
predicting protein,biological regulatory pathway,computational prediction,protein interactions,computational approach,biological pathway,protein interaction,computational approaches,global interaction pattern,protein interaction prediction,protein interaction data,data integration,protein-protein interactions · yeast two- hybrid · computational prediction,higher quality interaction map,protein protein interactions,data integrity,high throughput,yeast two hybrid,protein protein interaction,false positive,drug targeting,comparative genomics | Data integration,Data mining,Protein–protein interaction,Comparative genomics,False positives and false negatives,Medicine | Journal |
Volume | Issue | ISSN |
30 | 1 | 0148-5598 |
Citations | PageRank | References |
18 | 0.90 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingkai Yu | 1 | 70 | 4.20 |
| Farshad Fotouhi | 2 | 1023 | 122.73 |