Demo Data from EDBT 2026 / reviewers in the wild / expert
Qinghong Li
dblp:89/5539
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
0000-0002-2584-6024ORCID · reported
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
differential expression analysis |
0.0 | 1 | 2003 | The effect of replication on gene expression microarray experiments · Bioinform. 2003 |
Bioinformatics and computational biology
gene expression analysis |
0.0 | 1 | 2003 | The effect of replication on gene expression microarray experiments · Bioinform. 2003 |
Bioinformatics and computational biology › gene expression analysis
microarray |
0.0 | 1 | 2003 | The effect of replication on gene expression microarray experiments · Bioinform. 2003 |
Methods — techniques the papers use, named apart from their topics
statistical power analysis · 0.0random sampling · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | The effect of replication on gene expression microarray experimentsabstractMOTIVATION: We examine the effect of replication on the detection of apparently differentially expressed genes in gene expression microarray experiments. Our analysis is based on a random sampling approach using real data sets from 16 published studies. We consider both the ability to find genes that meet particular statistical criteria as well as the stability of the results in the face of changing levels of replication. RESULTS: While dependent on the data source, our findings suggest that stable results are typically not obtained until at least five biological replicates have been used. Conversely, for most studies, 10-15 replicates yield results that are quite stable, and there is less improvement in stability as the number of replicates is further increased. Our methods will be of use in evaluating existing data sets and in helping to design new studies. Paul Pavlidis, Qinghong Li, William Stafford Noble |
Bioinform. | 2 |