Demo Data from EDBT 2026 / reviewers in the wild / expert
Robert J. Clifford
dblp:07/3410
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2004
0000-0001-8888-897XORCID · reported
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
protein analysis |
0.0 | 1 | 2004 | Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms · Bioinform. 2004 |
Bioinformatics and computational biology
protein function prediction |
0.0 | 1 | 2004 | Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms · Bioinform. 2004 |
Bioinformatics and computational biology › statistical genetics
single nucleotide polymorphism analysis |
0.0 | 1 | 2004 | Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms · Bioinform. 2004 |
Bioinformatics and computational biology › genomics
variant analysis |
0.0 | 1 | 2004 | Large-scale analysis of non-synonymous coding region single nucleotide polymorphisms · Bioinform. 2004 |
Methods — techniques the papers use, named apart from their topics
pfam · 0.0hidden markov model · 0.0HMMER · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Large-scale analysis of non-synonymous coding region single nucleotide polymorphismsabstractMOTIVATION: Single nucleotide polymorphisms (SNPs) are the most common form of genetic variant in humans. SNPs causing amino acid substitutions are of particular interest as candidates for loci affecting susceptibility to complex diseases, such as diabetes and hypertension. To efficiently screen SNPs for disease association, it is important to distinguish neutral variants from deleterious ones. RESULTS: We describe the use of Pfam protein motif models and the HMMER program to predict whether amino acid changes in conserved domains are likely to affect protein function. We find that the magnitude of the change in the HMMER E-value caused by an amino acid substitution is a good predictor of whether it is deleterious. We provide internet-accessible display tools for a genomewide collection of SNPs, including 7391 distinct non-synonymous coding region SNPs in 2683 genes. AVAILABILITY: http://lpgws.nci.nih.gov/cgi-bin/GeneViewer.cgi Robert J. Clifford, Michael N. Edmonson, Cu Nguyen, Kenneth H. Buetow |
Bioinform. | 1 |