Demo Data from EDBT 2026 / reviewers in the wild / expert
Dawood B. Dudekula
dblp:30/4720
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
0000-0002-4054-1827ORCID · 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 · 56% Computational science and engineering · 44% |
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
microarray data analysis |
0.1 | 1 | 2005 | A web-based tool for principal component and significance analysis of microarray data · Bioinform. 2005 |
Computational science and engineering
statistical significance testing |
0.1 | 1 | 2005 | A web-based tool for principal component and significance analysis of microarray data · Bioinform. 2005 |
Bioinformatics and computational biology
gene expression analysis |
0.0 | 1 | 2005 | A web-based tool for principal component and significance analysis of microarray data · Bioinform. 2005 |
Methods — techniques the papers use, named apart from their topics
singular value decomposition · 0.1clustering · 0.1analysis of variance · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | A web-based tool for principal component and significance analysis of microarray dataabstractUNLABELLED: We have developed a program for microarray data analysis, which features the false discovery rate for testing statistical significance and the principal component analysis using the singular value decomposition method for detecting the global trends of gene-expression patterns. Additional features include analysis of variance with multiple methods for error variance adjustment, correction of cross-channel correlation for two-color microarrays, identification of genes specific to each cluster of tissue samples, biplot of tissues and corresponding tissue-specific genes, clustering of genes that are correlated with each principal component (PC), three-dimensional graphics based on virtual reality modeling language and sharing of PC between different experiments. The software also supports parameter adjustment, gene search and graphical output of results. The software is implemented as a web tool and thus the speed of analysis does not depend on the power of a client computer. AVAILABILITY: The tool can be used on-line or downloaded at http://lgsun.grc.nia.nih.gov/ANOVA/ Alexei A. Sharov, Dawood B. Dudekula, Minoru S. H. Ko |
Bioinform. | 2 |