Name
Abstract | ||
|---|---|---|
Dose-response information is critical to understanding drug effects, yet analytical methods for dose-response assays cannot cope with the dimensionality of large-scale screening data such as the microarray profiling data. To overcome this limitation, we developed and implemented the Sigmoidal Dose Response Search (SDRS) algorithm, a grid search-based method designed to handle large-scale dose-response data. This method not only calculates the pharmacological parameters for every assay, but also provides built-in statistic that enables downstream systematic analyses, such as characterizing dose response at the transcriptome level. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1093/bioinformatics/btr489 | BIOINFORMATICS |
Keywords | Field | DocType |
gene expression profiling,transcriptome,algorithms | Hyperparameter optimization,Data mining,Statistic,Profiling (computer programming),Computer science,Algorithm,Curse of dimensionality,Bioinformatics,Gene expression profiling | Journal |
Volume | Issue | ISSN |
27 | 20 | 1367-4803 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui-Ru Ji | 1 | 11 | 2.10 |
| Nathan O Siemers | 2 | 52 | 2.94 |
| Ming Lei | 3 | 0 | 0.34 |
| Liang Schweizer | 4 | 0 | 0.34 |
| Robert E. Bruccoleri | 5 | 51 | 30.69 |