Demo Data from EDBT 2026 / reviewers in the wild / expert
Clint L. Miller
dblp:176/3858
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0003-4276-3607ORCID · 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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics › ChIP-seq analysis
ChIP-seq data integration |
0.3 | 1 | 2018 | BART: a transcription factor prediction tool with query gene sets or epigenomic profiles · Bioinform. 2018 |
Bioinformatics and computational biology › gene regulation
gene regulation analysis |
0.3 | 1 | 2018 | BART: a transcription factor prediction tool with query gene sets or epigenomic profiles · Bioinform. 2018 |
Bioinformatics and computational biology › gene regulation › transcription factor analysis
transcription factor prediction |
0.3 | 1 | 2018 | BART: a transcription factor prediction tool with query gene sets or epigenomic profiles · Bioinform. 2018 |
Bioinformatics and computational biology
functional genomics |
0.1 | 1 | 2018 | BART: a transcription factor prediction tool with query gene sets or epigenomic profiles · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
gene set enrichment · 0.3ChIP-seq data mining · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | BART: a transcription factor prediction tool with query gene sets or epigenomic profilesabstractSummary: Identification of functional transcription factors that regulate a given gene set is an important problem in gene regulation studies. Conventional approaches for identifying transcription factors, such as DNA sequence motif analysis, are unable to predict functional binding of specific factors and not sensitive enough to detect factors binding at distal enhancers. Here, we present binding analysis for regulation of transcription (BART), a novel computational method and software package for predicting functional transcription factors that regulate a query gene set or associate with a query genomic profile, based on more than 6000 existing ChIP-seq datasets for over 400 factors in human or mouse. This method demonstrates the advantage of utilizing publicly available data for functional genomics research. Availability and implementation: BART is implemented in Python and available at http://faculty.virginia.edu/zanglab/bart. Supplementary information: Supplementary data are available at Bioinformatics online. Zhenjia Wang, Mete Civelek, Clint L. Miller, Nathan C. Sheffield, Michael J. Guertin, Chongzhi Zang |
Bioinform. | 3 |