Demo Data from EDBT 2026 / reviewers in the wild / expert
Allen Van Deynze
dblp:51/8321
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2010
0000-0002-2093-0577ORCID · reported
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › statistical genetics › genetic mapping
genetic map construction |
0.1 | 1 | 2010 | Single feature polymorphism detection using recombinant inbred line microarray expression data · Bioinform. 2010 |
Bioinformatics and computational biology › genomics › variant analysis
polymorphism detection |
0.1 | 1 | 2010 | Single feature polymorphism detection using recombinant inbred line microarray expression data · Bioinform. 2010 |
Bioinformatics and computational biology › statistical genetics
quantitative trait locus mapping |
0.1 | 1 | 2010 | Single feature polymorphism detection using recombinant inbred line microarray expression data · Bioinform. 2010 |
Methods — techniques the papers use, named apart from their topics
robust multi-array analysis · 0.1dip statistic · 0.1bootstrap hypothesis testing · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Single feature polymorphism detection using recombinant inbred line microarray expression dataabstractMOTIVATION: The Affymetrix GeneChip microarray is currently providing a high-density and economical platform for discovery of genetic polymorphisms. Microarray data for single feature polymorphism (SFP) detection in recombinant inbred lines (RILs) can capitalize on the high level of replication available for each locus in the RIL population. It was suggested that the binding affinities from all of the RILs would form a multimodal distribution for a SFP. This motivated us to estimate the binding affinities from the robust multi-array analysis (RMA) method and formulate the SFP detection problem as a hypothesis testing problem, i.e. testing whether the underlying distribution of the estimated binding affinity (EBA) values of a probe is unimodal or multimodal. RESULTS: We developed a bootstrap-based hypothesis testing procedure using the 'dip' statistic. Our simulation studies show that the proposed procedure can reach satisfactory detection power with false discovery rate controlled at a desired level and is robust to the unimodal distribution assumption, which facilitates wide application of the proposed procedure. Our analysis of the real data identified more than four times the SFPs compared to the previous studies, covering 96% of their findings. The constructed genetic map using the SFP markers predicted from our procedure shows over 99% concordance of the genetic orders of these markers with their known physical locations on the genome sequence. AVAILABILITY: The R package 'dipSFP' can be downloaded from http://sites.google.com/a/bioinformatics.ucr.edu/xinping-cui/home/software. Xinping Cui, Na You, Thomas Girke, Richard Michelmore, Allen Van Deynze |
Bioinform. | 5 |