Demo Data from VLDB 2026 / reviewers in the wild / expert
C. E. Penrod
dblp:185/8577
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 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
sequence analysis |
0.2 | 1 | 2016 | SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016 |
Bioinformatics and computational biology › genomics › repetitive DNA analysis
simple sequence repeat detection |
0.2 | 1 | 2016 | SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016 |
Algorithms and data structures › sequence algorithms
string algorithms |
0.2 | 1 | 2016 | SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016 |
Algorithms and data structures › sequence algorithms › string algorithms › string indexing
suffix array |
0.2 | 1 | 2016 | SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequences · Bioinform. 2016 |
Methods — techniques the papers use, named apart from their topics
longest common prefix array · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | SA-SSR: a suffix array-based algorithm for exhaustive and efficient SSR discovery in large genetic sequencesabstractUNLABELLED: Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software. AVAILABILITY AND IMPLEMENTATION: SA-SSR is freely available at http://github.com/ridgelab/SA-SSR CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Brandon D. Pickett, S. M. Karlinsey, C. E. Penrod, Michael J. Cormier, Mark T. W. Ebbert, Dennis K. Shiozawa, C. J. Whipple, Perry G. Ridge |
Bioinform. | 3 |