Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hoda Feizi
dblp:40/4183
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
microarray data analysis |
0.1 | 1 | 2008 | Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood · Bioinform. 2008 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood · 0.1ANOVA · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihoodabstractMOTIVATION: In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes. RESULTS: We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which nucleotide has been conjugated to the fluorescent dye. A method is proposed to account for gene-specific dye bias within a maximum-likelihood error modeling framework. Using two different labeling schemes, we show that correcting for gene-specific dye bias results in the superior identification of differentially expressed genes within this framework. Improvement is also possible in related ANOVA approaches. AVAILABILITY: A software implementation of this procedure is freely available at http://cellcircuits.org/VERA Ryan M. Kelley, Hoda Feizi, Trey Ideker |
Bioinform. | 2 |