Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nadine Hoecker
dblp:311/2811
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
gene expression analysis |
0.1 | 1 | 2006 | Combining signals from spotted cDNA microarrays obtained at different scanning intensities · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis
microarray data analysis |
0.1 | 1 | 2006 | Combining signals from spotted cDNA microarrays obtained at different scanning intensities · Bioinform. 2006 |
Methods — techniques the papers use, named apart from their topics
non-linear latent regression model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Combining signals from spotted cDNA microarrays obtained at different scanning intensitiesabstractMOTIVATION: The analysis of spotted cDNA microarrays involves scanning of color signals from fluorescent dyes. A common problem is that a given scanning intensity is not usually optimal for all spotted cDNAs. Specifically, some spots may be at the saturation limit, resulting in poor separation of signals from different tissues or conditions. The problem may be addressed by multiple scans with varying scanning intensities. Multiple scanning intensities raise the question of how to combine different signals from the same spot, particularly when measurement error is not negligible. RESULTS: This paper suggests a non-linear latent regression model for this purpose. It corrects for biases caused by the saturation limit and efficiently combines data from multiple scans. Combining multiple scans also allows reduction of technical error particularly for cDNA spots with low signal. The procedure is exemplified using cDNA expression data from maize. AVAILABILITY: All methods were implemented using standard procedures available in the SAS/STAT module of the SAS System. Programming statements are available from the first author upon request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: The supplementary data are available at Bioinformatics online. Hans-Peter Piepho, Barbara Keller, Nadine Hoecker, Frank Hochholdinger |
Bioinform. | 3 |