Name
Abstract | ||
|---|---|---|
We investigate the possibility of using pattern recognition techniques to classify various disease types using data produced by a new form of rapid Mass Spectrometry. The data format has several advantages over other high-throughput technologies and as such could become a useful diagnostic tool. We investigate the binary and multi-class performances obtained using standard classifiers as the number of features is varied and conclude that there is potential in this technique and suggest research directions that would improve performance. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11551188_20 | ICAPR (1) |
Keywords | Field | DocType |
high-throughput technology,research direction,pattern recognition technique,mass spectrometry,various disease type,disease classification,capillary electrophoresis,useful diagnostic tool,standard classifier,rapid mass spectrometry,new form,data format,multi-class performance,high throughput,pattern recognition | Capillary electrophoresis–mass spectrometry,Disease classification,Direct acyclic graph,Data format,Pattern recognition,Computer science,Data type,Mass spectrometry,Artificial intelligence,Distributed computing,Capillary electrophoresis | Conference |
Volume | ISSN | ISBN |
3686 | 0302-9743 | 3-540-28757-4 |
Citations | PageRank | References |
1 | 0.37 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Rogers | 1 | 54 | 3.69 |
| Mark Girolami | 2 | 1382 | 141.16 |
| Ronald Krebs | 3 | 1 | 0.37 |
| Harald Mischak | 4 | 15 | 2.31 |