Demo Data from EDBT 2026 / reviewers in the wild / expert
Vincent D. Wells
dblp:258/2735
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
genomics |
0.4 | 1 | 2020 | Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov models · Bioinform. 2020 |
Bioinformatics and computational biology › genomics › repetitive DNA analysis
microsatellite identification |
0.4 | 1 | 2020 | Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov models · Bioinform. 2020 |
Bioinformatics and computational biology
statistical genetics |
0.4 | 1 | 2020 | Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov models · Bioinform. 2020 |
Bioinformatics and computational biology › sequence analysis › repeat detection
tandem repeat detection |
0.4 | 1 | 2020 | Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov models · Bioinform. 2020 |
Methods — techniques the papers use, named apart from their topics
self-supervised hidden markov model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Look4TRs: a de novo tool for detecting simple tandem repeats using self-supervised hidden Markov modelsabstractMOTIVATION: Simple tandem repeats, microsatellites in particular, have regulatory functions, links to several diseases and applications in biotechnology. There is an immediate need for an accurate tool for detecting microsatellites in newly sequenced genomes. The current available tools are either sensitive or specific but not both; some tools require adjusting parameters manually. RESULTS: We propose Look4TRs, the first application of self-supervised hidden Markov models to discovering microsatellites. Look4TRs adapts itself to the input genomes, balancing high sensitivity and low false positive rate. It auto-calibrates itself. We evaluated Look4TRs on 26 eukaryotic genomes. Based on F measure, which combines sensitivity and false positive rate, Look4TRs outperformed TRF and MISA-the most widely used tools-by 78 and 84%. Look4TRs outperformed the second and the third best tools, MsDetector and Tantan, by 17 and 34%. On eight bacterial genomes, Look4TRs outperformed the second and the third best tools by 27 and 137%. AVAILABILITY AND IMPLEMENTATION: https://github.com/TulsaBioinformaticsToolsmith/Look4TRs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Alfredo Velasco II, Benjamin T. James, Vincent D. Wells, Hani Z. Girgis |
Bioinform. | 3 |