QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards
- Publikationstyp:
- Zeitschriftenaufsatz
- Metadaten:
-
- Autoren
- Jean F Fontaine
- Bernhard Suter
- Miguel A Andrade-Navarro
- DOI
- 10.1186/1756-0500-4-57
- eISSN
- 1756-0500
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- BMC Research Notes
- Sprache
- en
- Artikelnummer
- 57
- Online publication date
- 2011
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Herausgeber
- Springer Science and Business Media LLC
- Herausgeber URL
- http://dx.doi.org/10.1186/1756-0500-4-57
- Datum der Datenerfassung
- 2019
- Titel
- QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards
- Ausgabe der Zeitschrift
- 4
Datenquelle: Crossref
- Andere Metadatenquellen:
-
- Abstract
- <h4>Background</h4>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.<h4>Findings</h4>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.<h4>Conclusions</h4>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/.
- Addresses
- Max Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany. jean-fred.fontaine@mdc-berlin.de.
- Autoren
- Jean F Fontaine
- Bernhard Suter
- Miguel A Andrade-Navarro
- DOI
- 10.1186/1756-0500-4-57
- eISSN
- 1756-0500
- Externe Identifier
- PubMed Identifier: 21388526
- PubMed Central ID: PMC3060832
- Open access
- true
- ISSN
- 1756-0500
- Zeitschrift
- BMC research notes
- Sprache
- eng
- Medium
- Electronic
- Online publication date
- 2011
- Open access status
- Open Access
- Paginierung
- 57
- Datum der Veröffentlichung
- 2011
- Status
- Published
- Publisher licence
- CC BY
- Datum der Datenerfassung
- 2011
- Titel
- QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards.
- Sub types
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 4
Files
https://bmcresnotes.biomedcentral.com/track/pdf/10.1186/1756-0500-4-57 http://www.biomedcentral.com/content/pdf/1756-0500-4-57.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21388526/pdf/?tool=EBI https://europepmc.org/articles/PMC3060832?pdf=render
Datenquelle: Europe PubMed Central
- 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/.
- Date of acceptance
- 2011
- Autoren
- Jean F Fontaine
- Bernhard Suter
- Miguel A Andrade-Navarro
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/21388526
- DOI
- 10.1186/1756-0500-4-57
- eISSN
- 1756-0500
- Externe Identifier
- PubMed Central ID: PMC3060832
- Zeitschrift
- BMC Res Notes
- Sprache
- eng
- Country
- England
- Paginierung
- 57
- PII
- 1756-0500-4-57
- Datum der Veröffentlichung
- 2011
- Status
- Published online
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2011
- Titel
- QiSampler: evaluation of scoring schemes for high-throughput datasets using a repetitive sampling strategy on gold standards.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 4
Datenquelle: PubMed
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