Demo Data from VLDB 2026 / reviewers in the wild / expert
K. Ohm
dblp:42/5777
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
—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 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 |
0.0 | 1 | 2004 | GeneCluster 2.0: an advanced toolset for bioarray analysis · Bioinform. 2004 |
Bioinformatics and computational biology › gene expression analysis
sample classification |
0.0 | 1 | 2004 | GeneCluster 2.0: an advanced toolset for bioarray analysis · Bioinform. 2004 |
Bioinformatics and computational biology › gene expression analysis › gene expression classification
marker gene selection |
0.0 | 1 | 2004 | GeneCluster 2.0: an advanced toolset for bioarray analysis · Bioinform. 2004 |
Methods — techniques the papers use, named apart from their topics
weighted voting · 0.0self-organizing map · 0.0permutation test · 0.0k-nearest neighbor · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | GeneCluster 2.0: an advanced toolset for bioarray analysisabstractSUMMARY: GeneCluster 2.0 is a software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding classification, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting and k-nearest neighbor algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater. AVAILABILITY: http://www.broad.mit.edu/cancer/software Michael Reich, K. Ohm, Michael Angelo, Pablo Tamayo, Jill P. Mesirov |
Bioinform. | 2 |