Demo Data from SIGIR 2026 / reviewers in the wild / expert
Philip J. Cotter
dblp:43/5820
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2002
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › comparative genomics › orthology analysis
ortholog identification |
0.0 | 1 | 2002 | Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002 |
Bioinformatics and computational biology › sequence analysis › sequence similarity search
sequence database search |
0.0 | 1 | 2002 | Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002 |
Bioinformatics and computational biology
phylogenetics |
0.0 | 1 | 2002 | Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002 |
Methods — techniques the papers use, named apart from their topics
outgroup conditioning · 0.0log-odds scoring · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | Improved database searches for orthologous sequences by conditioning on outgroup sequencesabstractAbstract Motivation: Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region. Results: Such inferences are shown to be improved by conditioning the search results on the scores of an outgroup sequence. The log-odds score for each target sequence identified on the database has the log-odds score of the outgroup sequence subtracted from it. A test group of Caenorhabditis elegans kinase sequences and their identified C.elegans outgroups were searched against a test database of human Expressed Sequence Tag (EST) sequences, where the sets of true target sequences were known in advance. The outgroup conditioned method was shown to identify 58% more true positives ahead of the first false positive, compared to the straightforward search without an outgroup. A test dataset of 151 proteins drawn from the C.elegans genome, where the putative ‘outgroup’ was assigned automatically, similarly found 50% more true positives using outgroup conditioning. Thus, outgroup conditioning provides a means to improve the results of database searching with little increase in the search computation time. Availability: Perl scripts for the Outgroup Conditioned Score (OCS) method are available without charge for non-profit academic use from http://www.bioinf.org/vibe/software/OCS/. Scripts have been optimized for Linux or OSF with a Perl v5 interpreter. Contact: [email protected]. * Non-standard abbreviations: OCS: Outgroup Conditioned Score, HOCS Heuristic Outgroup Conditioned Score, HOCP: Heuristic Outgroup Conditioned P-value ratio, EST: Expressed Sequence Tag. 1 To whom correspondence should be addressed. Philip J. Cotter, Daniel R. Caffrey, Denis C. Shields |
Bioinform. | 1 |