Demo Data from VLDB 2026 / reviewers in the wild / expert
Jason E. Donald
dblp:04/5751
· DBLP profile ↗
2ranked-venue papers
2as 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 · 2 · 2 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
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
protein function prediction |
0.1 | 1 | 2005 | Determining functional specificity from protein sequences · Bioinform. 2005 |
Bioinformatics and computational biology › protein sequence analysis
residue conservation |
0.1 | 1 | 2005 | CoC: a database of universally conserved residues in protein folds · Bioinform. 2005 |
Methods — techniques the papers use, named apart from their topics
sequence clustering · 0.1TRIBE-MCL · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | CoC: a database of universally conserved residues in protein foldsabstractSummary: The conservatism of conservatism (CoC) database presents statistically analyzed information about the conservation of residue positions in folds across protein families. Availability: On the web at http://kulibin.mit.edu/coc/ Contact: [email protected] Supplementary information: The website details the method and contains an FAQ and documentation, http://kulibin.mit.edu/coc/ Jason E. Donald, Isaac A. Hubner, Veronica Rotemberg, Eugene I. Shakhnovich, Leonid A. Mirny |
Bioinform. | 1 |
| 2005 | Determining functional specificity from protein sequencesabstractMotivation: Given a large family of homologous protein sequences, many methods can divide the family into smaller groups that correspond to the different functions carried out by proteins within the family. One important problem, however, has been the absence of a general method for selecting an appropriate level of granularity, or size of the groups. Results: We propose a consistent way of choosing the granularity that is independent of the sequence similarity and sequence clustering method used. We study three large, well-investigated protein families: basic leucine zippers, nuclear receptors and proteins with three consecutive C2H2 zinc fingers. Our method is tested against known functional information, the experimentally determined binding specificities, using a simple scoring method. The significance of the groups is also measured by randomizing the data. Finally, we compare our algorithm against a popular method of grouping proteins, the TRIBE-MCL method. In the end, we determine that dividing the families at the proposed level of granularity creates very significant and useful groups of proteins that correspond to the different DNA-binding motifs. We expect that such groupings will be useful in studying not only DNA binding but also other protein interactions. Contact: [email protected] Supplementary information: The supplementary material contains: experimental binding specificities, a list of proteins in the proposed clusters, a table listing the percentage of proteins with binding data and from humans, visualizations of nuclear receptor and zinc finger proteins from humans, gene trees for two families, BLAST results and TRIBE-MCL results. Jason E. Donald, Eugene I. Shakhnovich |
Bioinform. | 1 |