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 VLDB 2026 / reviewers in the wild / expert

Jeremy D. Burgess

dblp:440/7184 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene regulation		 1.012026
SEMPLR: an R package for transcription factor binding prediction · Bioinform. 2026
Bioinformatics and computational biology
genomics		 1.012026
SEMPLR: an R package for transcription factor binding prediction · Bioinform. 2026
Bioinformatics and computational biology › gene regulation
transcription factor binding site prediction		 1.012026
SEMPLR: an R package for transcription factor binding prediction · Bioinform. 2026
Bioinformatics and computational biology › statistical genetics
variant effect prediction		 1.012026
SEMPLR: an R package for transcription factor binding prediction · Bioinform. 2026
Bioinformatics and computational biology › gene regulation
regulatory sequence analysis		 0.312026
SEMPLR: an R package for transcription factor binding prediction · Bioinform. 2026

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

SNP effect matrices · 1.0
YearPublicationVenuePosition
2026 SEMPLR: an R package for transcription factor binding prediction
abstract
SUMMARY: SEMPLR is an R package that predicts transcription factor binding and variant effects using SNP Effect Matrices (SEMs), providing efficient, genome-wide scoring, enrichment testing, and visualization tools for comprehensive analysis of regulatory sequences. AVAILABILITY: Available on GitHub at https://github.com/grkenney/SEMPLR and on Bioconductor at https://bioconductor.org/packages/release/bioc/html/SEMPLR.html.
Grace E. Kenney, Rintsen N. Sherpa, Jeremy D. Burgess, Alan P. Boyle, Douglas H. Phanstiel
 Bioinform.3