Demo Data from EDBT 2026 / reviewers in the wild / expert
Sihaela Salzberg
dblp:65/4704
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2005
—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 › genome annotation › gene prediction
comparative gene finding |
0.1 | 1 | 2005 | Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005 |
Bioinformatics and computational biology › genome annotation
gene prediction |
0.1 | 1 | 2005 | Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005 |
Bioinformatics and computational biology › genome annotation › gene prediction
homology-based gene prediction |
0.1 | 1 | 2005 | Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005 |
Bioinformatics and computational biology › phylogenetics › phylogenetic inference › statistical alignment
pair hidden markov model |
0.1 | 1 | 2005 | Efficient implementation of a generalized pair hidden Markov model for comparative gene finding · Bioinform. 2005 |
Methods — techniques the papers use, named apart from their topics
generalized pair hidden markov model · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Efficient implementation of a generalized pair hidden Markov model for comparative gene findingabstractMOTIVATION: The increased availability of genome sequences of closely related organisms has generated much interest in utilizing homology to improve the accuracy of gene prediction programs. Generalized pair hidden Markov models (GPHMMs) have been proposed as one means to address this need. However, all GPHMM implementations currently available are either closed-source or the details of their operation are not fully described in the literature, leaving a significant hurdle for others wishing to advance the state of the art in GPHMM design. RESULTS: We have developed an open-source GPHMM gene finder, TWAIN, which performs very well on two related Aspergillus species, A.fumigatus and A.nidulans, finding 89% of the exons and predicting 74% of the gene models exactly correctly in a test set of 147 conserved gene pairs. We describe the implementation of this GPHMM and we explicitly address the assumptions and limitations of the system. We suggest possible ways of relaxing those assumptions to improve the utility of the system without sacrificing efficiency beyond what is practical. AVAILABILITY: Available at http://www.tigr.org/software/pirate/twain/twain.html under the open-source Artistic License. William H. Majoros, Mihaela Pertea, Sihaela Salzberg |
Bioinform. | 3 |