Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Reinhard Schuh

dblp:77/430 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · unresolved

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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis		 0.012004
Gene-Ontology-based clustering of gene expression data · Bioinform. 2004
Bioinformatics and computational biology › gene expression analysis
gene expression clustering		 0.012004
Gene-Ontology-based clustering of gene expression data · Bioinform. 2004
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene ontology analysis		 0.012004
Gene-Ontology-based clustering of gene expression data · Bioinform. 2004
Bioinformatics and computational biology
ontology-based clustering		 0.012004
Gene-Ontology-based clustering of gene expression data · Bioinform. 2004

Methods — techniques the papers use, named apart from their topics

numerical clustering · 0.0
YearPublicationVenuePosition
2004 Gene-Ontology-based clustering of gene expression data
abstract
Abstract Summary: The expected correlation between genetic co-regulation and affiliation to a common biological process is not necessarily the case when numerical cluster algorithms are applied to gene expression data. GO-Cluster uses the tree structure of the Gene Ontology database as a framework for numerical clustering, and thus allowing a simple visualization of gene expression data at various levels of the ontology tree. Availability: The 32-bit Windows application is freely available at http://www.mpibpc.mpg.de/go-cluster/ Supplementary information: A comprehensive software manual, hints for troubleshooting and commented examples are available from the website.
Boris Adryan, Reinhard Schuh
 Bioinform.2