Name
Abstract | ||
|---|---|---|
A central issue in biological data analysis is that uncertainty, resulting from different factors of variability, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.procs.2015.05.185 | Procedia Computer Science |
Keywords | Field | DocType |
Data analysis,Inference,Robust decisions,Graph,Mass spectrometry | Biological data,Population,Data mining,Random graph,Graph property,Reference model,Inference,Computer science,Robustness (computer science),Artificial intelligence,Mass spectrometry,Machine learning | Conference |
Volume | ISSN | Citations |
51 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Italo Zoppis | 1 | 38 | 18.39 |
| Riccardo Dondi | 2 | 89 | 18.42 |
| Massimiliano Borsani | 3 | 7 | 2.41 |
| Erica Gianazza | 4 | 6 | 2.35 |
| Clizia Chinello | 5 | 7 | 2.70 |
| Fulvio Magni | 6 | 7 | 2.70 |
| Giancarlo Mauri | 7 | 2106 | 297.38 |