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Qinghong Li

dblp:89/5539 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
differential expression analysis
0.012003
The effect of replication on gene expression microarray experiments · Bioinform. 2003
Bioinformatics and computational biology
gene expression analysis
0.012003
The effect of replication on gene expression microarray experiments · Bioinform. 2003
Bioinformatics and computational biology › gene expression analysis
microarray
0.012003
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
YearPublicationVenuePosition
2003 The effect of replication on gene expression microarray experiments
abstract
MOTIVATION: 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