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| Item Type: | Article |
|---|---|
| Title: | QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards |
| Creators Name: | Fontaine, J.F., Suter, B. and Andrade-Navarro, M.A. |
| Abstract: | BACKGROUND: High-throughput biological experiments can produce a large amount of data showing little overlap with current knowledge. This may be a problem when evaluating alternative scoring mechanisms for such data according to a gold standard dataset because standard statistical tests may not be appropriate. FINDINGS: To address this problem we have implemented the QiSampler tool that uses a repetitive sampling strategy to evaluate several scoring schemes or experimental parameters for any type of high-throughput data given a gold standard. We provide two example applications of the tool: selection of the best scoring scheme for a high-throughput protein-protein interaction dataset by comparison to a dataset derived from the literature, and evaluation of functional enrichment in a set of tumour-related differentially expressed genes from a thyroid microarray dataset. CONCLUSIONS: QiSampler is implemented as an open source R script and a web server, which can be accessed at http://cbdm.mdc-berlin.de/tools/sampler/. |
| Source: | BMC Research Notes |
| ISSN: | 1756-0500 |
| Publisher: | BioMed Central |
| Volume: | 4 |
| Number: | 1 |
| Page Range: | 57 |
| Date: | 9 March 2011 |
| Official Publication: | https://doi.org/10.1186/1756-0500-4-57 |
| PubMed: | View item in PubMed |
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